U.S. patent application number 17/163893 was filed with the patent office on 2021-05-27 for patient management based on anatomic measurements.
The applicant listed for this patent is Siemens Healthcare GmbH. Invention is credited to Luca Bogoni, Gerardo Hermosillo Valadez, Shu Liao, Zhigang Peng, Marcos Salganicoff, Matthias Wolf, Yiqiang Zhan, Xiang Sean Zhou.
Application Number | 20210158531 17/163893 |
Document ID | / |
Family ID | 1000005373866 |
Filed Date | 2021-05-27 |
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United States Patent
Application |
20210158531 |
Kind Code |
A1 |
Bogoni; Luca ; et
al. |
May 27, 2021 |
Patient Management Based On Anatomic Measurements
Abstract
A framework for patient management based on anatomic
measurements is described herein. In accordance with one aspect,
patient records are clustered into a set of sub-populations based
on first anatomic measurements and characteristics extracted from
first patient data associated with a population of patients. A
representative sub-population similar to a patient may be
determined from the set of sub-populations based on the patient
data of the patient. A report that presents the second anatomic
measurements associated with the patient in relation to
corresponding first anatomic measurements associated with the
representative sub-population may then be generated.
Inventors: |
Bogoni; Luca; (Philadelphia,
PA) ; Salganicoff; Marcos; (Bala Cynwyd, PA) ;
Wolf; Matthias; (Coatesville, PA) ; Liao; Shu;
(Chester Springs, PA) ; Zhan; Yiqiang; (West
Chester, PA) ; Hermosillo Valadez; Gerardo; (West
Chester, PA) ; Zhou; Xiang Sean; (Exton, PA) ;
Peng; Zhigang; (Ambler, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Siemens Healthcare GmbH |
Erlangen |
|
DE |
|
|
Family ID: |
1000005373866 |
Appl. No.: |
17/163893 |
Filed: |
February 1, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
15088305 |
Apr 1, 2016 |
10949975 |
|
|
17163893 |
|
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62146490 |
Apr 13, 2015 |
|
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62153643 |
Apr 28, 2015 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 2207/10116
20130101; G16H 10/60 20180101; G06T 2207/10081 20130101; G06N 3/04
20130101; A61B 5/055 20130101; G16H 15/00 20180101; G06T 7/11
20170101; G16H 30/40 20180101; A61B 5/1075 20130101; G06T 7/0012
20130101; G16H 50/20 20180101; A61B 5/4566 20130101; G06T
2207/20081 20130101; G06T 2207/30012 20130101 |
International
Class: |
G06T 7/11 20060101
G06T007/11; G06T 7/00 20060101 G06T007/00; G16H 15/00 20060101
G16H015/00; A61B 5/00 20060101 A61B005/00; G16H 50/20 20060101
G16H050/20; G16H 10/60 20060101 G16H010/60; A61B 5/055 20060101
A61B005/055; A61B 5/107 20060101 A61B005/107; G16H 30/40 20060101
G16H030/40 |
Claims
1. A system for patient management, comprising: a non-transitory
memory device for storing computer readable program code; and a
processor in communication with the memory device, the processor
being operative with the computer readable program code to perform
operations including clustering patient records into a set of
sub-populations based on first anatomic measurements and
characteristics extracted from first patient data associated with a
population of patients, receiving second patient data of a patient,
wherein the second patient data comprises image data and associated
second anatomic measurements of at least one structure of interest,
determining, from the set of sub-populations, a representative
sub-population similar to the patient based on the second patient
data, and generating a report that presents the second anatomic
measurements in relation to corresponding first anatomic
measurements associated with the representative sub-population.
2. The system of claim 1 wherein the processor is operative with
the computer readable program code to automatically generate the
first or second anatomic measurements.
3. The system of claim 2 wherein the first or second anatomic
measurements comprise an anterior shift of a vertebra.
4. The system of claim 2 wherein the first or second anatomic
measurements comprise an aortic diameter.
5. The system of claim 1 wherein the processor is operative with
the computer readable program code to automatically generate the
first or second anatomic measurements based on first anatomic
landmarks pre-defined according to well-established guidelines.
6. The system of claim 5 wherein the processor is operative with
the computer readable program code to automatically generate the
first or second anatomic measurements based on second anatomic
landmarks along a continuum of locations to facilitate early
identification of onset of disease.
7. The system of claim 1 wherein the processor is operative with
the computer readable program code to cluster the patient records
according to clinical fields determined by a type or location of
structures of interest measured by the first anatomic
measurements.
8. The system of claim 1 wherein the processor is operative with
the computer readable program code to cluster the patient records
according to degrees of disease derived from the first anatomic
measurements.
9. The system of claim 1 wherein the processor is operative with
the computer readable program code to associate at least one
sub-population from the set of sub-populations with one or more
treatments.
10. The system of claim 9 wherein the processor is operative with
the computer readable program code to associate at least one of the
one or more treatments with one or more outcomes.
11. The system of claim 10 wherein the processor is operative with
the computer readable program code to present a recommendation of
the one or more treatments based on the one or more outcomes
associated with the representative sub-population.
12. The system of claim 1 wherein the processor is operative with
the computer readable program code to compare the second anatomic
measurements with the first anatomic measurements to identify
differences between the patient and the sub-population.
13. The system of claim 1 wherein the processor is operative with
the computer readable program code to compare the second anatomic
measurements with each other to identify any lack of symmetry,
anomaly or difference from other similar anatomic structures in the
patient.
14. The system of claim 1 wherein the report presents a summary
view of the first and second anatomic measurements measured at
pre-defined anatomic landmarks.
15. The system of claim 1 wherein the report further presents the
second anatomic measurements in relation to characteristics
associated with the representative sub-population.
16. The system of claim 15 wherein the characteristics comprise
age, gender or body surface area.
17. The system of claim 1 wherein the processor is operative with
the computer readable program code to present the report in a
dashboard environment.
18. A method, comprising: receiving patient data of a patient,
wherein the patient data comprises image data of at least one
structure of interest; automatically generating first anatomic
measurements of the structure of interest; determining, based on
the patient data and the first anatomic measurements, a
representative sub-population of patients similar to the patient
from a database of patient records; and generating a report that
presents the first anatomic measurements in relation to
corresponding second anatomic measurements associated with the
representative sub-population.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a divisional of U.S. application
Ser. No. 15/088,305 filed Apr. 1, 2016 which claims the benefit of
U.S. provisional application No. 62/146,490 filed Apr. 13, 2015 and
U.S. provisional application No. 62/153,643 filed Apr. 28, 2015,
the entire contents of which are herein incorporated by
reference.
TECHNICAL FIELD
[0002] The present disclosure generally relates to medical data
processing, and more particularly to patient management based on
anatomic measurements.
BACKGROUND
[0003] The field of medical imaging has seen significant advances
since the time X-Rays were first used to determine anatomic
abnormalities. Medical imaging hardware has progressed in the form
of newer machines such as Magnetic Resonance Imaging (MRI)
scanners, Computed Axial Tomography (CAT) scanners, etc. Because of
the large amount of image data generated by such modern medical
scanners, there has been and remains a need for developing image
processing techniques that can automate some or all of the
processes to determine the presence of anatomic abnormalities in
scanned medical images.
[0004] Digital medical images are constructed using raw image data
obtained from a scanner, for example, a CAT scanner, MRI, etc.
Digital medical images are typically either a two-dimensional
("2D") image made of pixel elements or a three-dimensional ("3D")
image made of volume elements ("voxels"). Such 2D or 3D images are
processed using medical image recognition techniques to determine
the presence of anatomic structures such as cysts, tumors, polyps,
etc. Given the amount of image data generated by any given image
scan, it is preferable that an automatic technique should point out
anatomic features in the selected regions of an image to a doctor
for further diagnosis of any disease or condition.
[0005] Automatic image processing and recognition of structures
within a medical image is generally referred to as Computer-Aided
Detection (CAD). A CAD system can process medical images and
identify anatomic structures, including possible abnormalities, for
further review. Such possible abnormalities are often called
candidates and are considered to be generated by the CAD system
based upon the medical images.
[0006] Technicians, radiologists and physicians typically perform
anatomic measurements multiple times daily on medical images to
assess the state of a specific anatomic structure and/or determine
whether the measurements are within acceptable parameters. To
enhance efficiency and reduce inter- and intra-reader variability,
automated measurement tools have been designed for routine, as well
as specialized, studies. However, such automated measurement tools
are typically not available for measuring certain types of medical
conditions.
[0007] One such medical condition is spondylolisthesis.
Spondylolisthesis is one of the most common spinal diseases. It is
caused by the anterior shift of one vertebra over its subjacent
vertebra due to various causes such as fracture, degenerative disc,
congenital reasons, etc. Patients with spondylolisthesis often
suffer from severe lower back pain. The clinical workflow and
treatment methods for spondylolisthesis may vary depending on how
severe (i.e., the grade) the condition is. For instance,
conservative treatment methods, such as physical therapy and
resting, are often applied to patients with low-grade
spondylolisthesis, while surgery may be necessary for patients with
high-grade spondylolisthesis.
[0008] In current clinical workflows, anterior shift of the
vertebra is manually measured to determine the degree of
spondylolisthesis. Such manual measurements are time-consuming and
typically not reproducible. Additionally, such measurements are
often directly presented to the physician, without any reference to
representative values to support patient management. The physician
needs to rely on his or her own experience and knowledge to
determine the correct course of action. At times, the physician may
search the literature or web to identify the correct course of
action. This is an inherent limitation in a busy clinical
practice.
SUMMARY
[0009] Described herein is a framework for patient management based
on anatomic measurements. In accordance with one aspect, patient
records are clustered into a set of sub-populations based on first
anatomic measurements and characteristics extracted from first
patient data associated with a population of patients. The
framework may then receive second patient data of a patient,
wherein the second patient data comprises image data and associated
second anatomic measurements of at least one structure of interest.
A representative sub-population similar to the patient may then be
determined from the set of sub-populations based on the second
patient data. A report that presents the second anatomic
measurements in relation to corresponding first anatomic
measurements associated with the representative sub-population may
be generated.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] A more complete appreciation of the present disclosure and
many of the attendant aspects thereof will be readily obtained as
the same becomes better understood by reference to the following
detailed description when considered in connection with the
accompanying drawings.
[0011] FIG. 1 is a block diagram illustrating an exemplary
system;
[0012] FIG. 2 shows an exemplary method of performing automated
measurements by a computer system;
[0013] FIG. 3 illustrates an exemplary automatic measurement of
anterior shift;
[0014] FIG. 4 illustrates another exemplary automatic measurement
of anterior shift;
[0015] FIG. 5 shows exemplary medical images illustrating automatic
measurements of anterior shift for normal patients;
[0016] FIG. 6 shows exemplary medical images illustrating automatic
measurements of anterior shift for grade 1 spondylolisthesis
patients;
[0017] FIG. 7 shows exemplary medical images illustrating automatic
measurements of anterior shift for grade 2 spondylolisthesis
patients;
[0018] FIG. 8 shows an exemplary method of building a database by a
computer system;
[0019] FIG. 9 shows an exemplary clustering of a collection of
patient records;
[0020] FIG. 10 shows an exemplary method of generating a report by
a computer system;
[0021] FIG. 11 show exemplary anatomic landmark locations according
to the American College of Cardiology (ACC) and the American Heart
Association (AHA) guidelines for diagnosis and management of
patients with thoracic aortic disease;
[0022] FIG. 12 illustrates an exemplary chart in a summary
view;
[0023] FIG. 13 illustrates the influence of aortic size on
cumulative and lifetime incidences of natural complications of
aortic aneurysm;
[0024] FIG. 14a shows a chart depicting measurements of aortic
diameter as a function of body surface area (BSA); and
[0025] FIG. 14b shows a chart depicting measurements of aortic
diameter as a function of BSA at the ascending location for male
patients.
DETAILED DESCRIPTION
[0026] In the following description, numerous specific details are
set forth such as examples of specific components, devices,
methods, etc., in order to provide a thorough understanding of
implementations of the present framework. It will be apparent,
however, to one skilled in the art that these specific details need
not be employed to practice implementations of the present
framework. In other instances, well-known materials or methods have
not been described in detail in order to avoid unnecessarily
obscuring implementations of the present framework. While the
present framework is susceptible to various modifications and
alternative forms, specific embodiments thereof are shown by way of
example in the drawings and will herein be described in detail. It
should be understood, however, that there is no intent to limit the
invention to the particular forms disclosed; on the contrary, the
intention is to cover all modifications, equivalents, and
alternatives falling within the spirit and scope of the invention.
Furthermore, for ease of understanding, certain method steps are
delineated as separate steps; however, these separately delineated
steps should not be construed as necessarily order dependent in
their performance.
[0027] The term "x-ray image" as used herein may mean a visible
x-ray image (e.g., displayed on a video screen) or a digital
representation of an x-ray image (e.g., a file corresponding to the
pixel output of an x-ray detector). The term "in-treatment x-ray
image" as used herein may refer to images captured at any point in
time during a treatment delivery phase of an interventional or
therapeutic procedure, which may include times when the radiation
source is either on or off. From time to time, for convenience of
description, CT imaging data (e.g., cone-beam CT imaging data) may
be used herein as an exemplary imaging modality. It will be
appreciated, however, that data from any type of imaging modality
including but not limited to x-ray radiographs, MM, PET (positron
emission tomography), PET-CT, SPECT, SPECT-CT, MR-PET, 3D
ultrasound images or the like may also be used in various
implementations.
[0028] Unless stated otherwise as apparent from the following
discussion, it will be appreciated that terms such as "segmenting,"
"generating," "registering," "determining," "aligning,"
"positioning," "processing," "computing," "selecting,"
"estimating," "detecting," "tracking" or the like may refer to the
actions and processes of a computer system, or similar electronic
computing device, that manipulates and transforms data represented
as physical (e.g., electronic) quantities within the computer
system's registers and memories into other data similarly
represented as physical quantities within the computer system
memories or registers or other such information storage,
transmission or display devices. Embodiments of the methods
described herein may be implemented using computer software. If
written in a programming language conforming to a recognized
standard, sequences of instructions designed to implement the
methods can be compiled for execution on a variety of hardware
platforms and for interface to a variety of operating systems. In
addition, implementations of the present framework are not
described with reference to any particular programming language. It
will be appreciated that a variety of programming languages may be
used.
[0029] As used herein, the term "image" refers to multi-dimensional
data composed of discrete image elements (e.g., pixels for 2D
images and voxels for 3D images). The image may be, for example, a
medical image of a subject collected by computer tomography,
magnetic resonance imaging, ultrasound, or any other medical
imaging system known to one of skill in the art. The image may also
be provided from non-medical contexts, such as, for example, remote
sensing systems, electron microscopy, etc. Although an image can be
thought of as a function from R.sup.3 to R, or a mapping to
R.sup.3, the present methods are not limited to such images, and
can be applied to images of any dimension, e.g., a 2D picture or a
3D volume. For a 2- or 3-dimensional image, the domain of the image
is typically a 2- or 3Dimensional rectangular array, wherein each
pixel or voxel can be addressed with reference to a set of 2 or 3
mutually orthogonal axes. The terms "digital" and "digitized" as
used herein will refer to images or volumes, as appropriate, in a
digital or digitized format acquired via a digital acquisition
system or via conversion from an analog image.
[0030] The terms "pixels" for picture elements, conventionally used
with respect to 2D imaging and image display, and "voxels" for
volume image elements, often used with respect to 3D imaging, can
be used interchangeably. It should be noted that the 3D volume
image is itself synthesized from image data obtained as pixels on a
2D sensor array and displayed as a 2D image from some angle of
view. Thus, 2D image processing and image analysis techniques can
be applied to the 3D volume image data. In the description that
follows, techniques described as operating upon pixels may
alternately be described as operating upon the 3D voxel data that
is stored and represented in the form of 2D pixel data for display.
In the same way, techniques that operate upon voxel data can also
be described as operating upon pixels. In the following
description, the variable x is used to indicate a subject image
element at a particular spatial location or, alternately
considered, a subject pixel. The terms "subject pixel" or "subject
voxel" are used to indicate a particular image element as it is
operated upon using techniques described herein.
[0031] A framework for patient management based on anatomic
measurements is described herein. In accordance with one aspect,
the framework automatically determines the level of
spondylolisthesis. More particularly, the framework automatically
measures the anterior shift of one vertebra over its subjacent
vertebra in the medical image data. The grade of spondylolisthesis
may be determined based on established guidelines (e.g., Meyerding
grading system).
[0032] In accordance with another aspect of the framework, anatomic
measurements are performed in accordance with standard guidelines,
and used to support patient management. The anatomic measurements
of a given patient may be presented in relation to a representative
sub-population of patients with similar characteristics and/or
anatomic measurement values. In some implementations, the anatomic
measurements are presented in relation to the anatomical
measurements, characteristics, treatments, outcomes, and/or other
information extracted from patient records associated with the
representative sub-population.
[0033] By automating measurements, the present framework
advantageously reduces intra- and inter-reader variability in
performing measurements. Efficiency is improved by changing the
role of the user (e.g., physician, radiologist) from performing a
measurement to reviewing and verifying the measurements. Reporting
aspects may be enhanced, since structured data can be automatically
populated in case reports and sent to registries or other parties
as needed. A more accurate view of the anatomy or disease may be
provided. These and other exemplary advantages and features will be
described in more details herein.
[0034] FIG. 1 is a block diagram illustrating an exemplary system
100. The system 100 includes a computer system 101 for implementing
the framework as described herein. In some implementations,
computer system 101 operates as a standalone device. In other
implementations, computer system 101 may be connected (e.g., using
a network) to other machines, such as imaging device 102 and
workstation 103. In a networked deployment, computer system 101 may
operate in the capacity of a server (e.g., thin-client server, such
as syngo.via.RTM. by Siemens Healthcare), a cloud computing
platform, a client user machine in server-client user network
environment, or as a peer machine in a peer-to-peer (or
distributed) network environment.
[0035] In some implementations, computer system 101 comprises a
processor or central processing unit (CPU) 104 coupled to one or
more non-transitory computer-readable media 105 (e.g., computer
storage or memory), display device 110 (e.g., monitor) and various
input devices 111 (e.g., mouse or keyboard) via an input-output
interface 121. Computer system 101 may further include support
circuits such as a cache, a power supply, clock circuits and a
communications bus. Various other peripheral devices, such as
additional data storage devices and printing devices, may also be
connected to the computer system 101.
[0036] The present technology may be implemented in various forms
of hardware, software, firmware, special purpose processors, or a
combination thereof, either as part of the microinstruction code or
as part of an application program or software product, or a
combination thereof, which is executed via the operating system. In
some implementations, the techniques described herein are
implemented as computer-readable program code tangibly embodied in
non-transitory computer-readable media 105. In particular, the
present techniques may be implemented by a measurement tool 106, a
database builder 107, a report generator 108 and a database
109.
[0037] Non-transitory computer-readable media 105 may include
random access memory (RAM), read-only memory (ROM), magnetic floppy
disk, flash memory, and other types of memories, or a combination
thereof. The computer-readable program code is executed by CPU 104
to process medical data retrieved from, for example, imaging device
102. As such, the computer system 101 is a general-purpose computer
system that becomes a specific purpose computer system when
executing the computer-readable program code. The computer-readable
program code is not intended to be limited to any particular
programming language and implementation thereof. It will be
appreciated that a variety of programming languages and coding
thereof may be used to implement the teachings of the disclosure
contained herein.
[0038] The same or different computer-readable media 105 may be
used for storing a database (or dataset) 109. Such data may also be
stored in external storage or other memories. The external storage
may be implemented using a database management system (DBMS)
managed by the CPU 104 and residing on a memory, such as a hard
disk, RAM, or removable media. The external storage may be
implemented on one or more additional computer systems. For
example, the external storage may include a data warehouse system
residing on a separate computer system, a cloud platform or system,
a picture archiving and communication system (PACS), or any other
hospital, medical institution, medical office, testing facility,
pharmacy or other medical patient record storage system.
[0039] Imaging device 102 acquires medical image data 120
associated with at least one patient. Such medical image data 120
may be processed and stored in database 109. Imaging device 102 may
be a radiology scanner (e.g., X-ray, MR or a CT scanner) and/or
appropriate peripherals (e.g., keyboard and display device) for
acquiring, collecting and/or storing such medical image data
120.
[0040] The workstation 103 may include a computer and appropriate
peripherals, such as a keyboard and display device, and can be
operated in conjunction with the entire system 100. For example,
the workstation 103 may communicate directly or indirectly with the
imaging device 102 so that the medical image data acquired by the
imaging device 102 can be rendered at the workstation 103 and
viewed on a display device. The workstation 103 may also provide
other types of medical data 122 of a given patient currently
undergoing examination. The workstation 103 may include a graphical
user interface to receive user input via an input device (e.g.,
keyboard, mouse, touch screen voice or video recognition interface,
etc.) to input the current medical data 122.
[0041] It is to be further understood that, because some of the
constituent system components and method steps depicted in the
accompanying figures can be implemented in software, the actual
connections between the systems components (or the process steps)
may differ depending upon the manner in which the present framework
is programmed. Given the teachings provided herein, one of ordinary
skill in the related art will be able to contemplate these and
similar implementations or configurations of the present
framework.
[0042] FIG. 2 shows an exemplary method 200 of performing automated
measurements by a computer system. It should be understood that the
steps of the method 200 may be performed in the order shown or a
different order. Additional, different, or fewer steps may also be
provided. Further, the method 200 may be implemented with the
system 101 of FIG. 1, a different system, or a combination
thereof.
[0043] At 204, measurement tool 106 receives image data of at least
a portion of a spine (or vertebral column) of a given patient. The
image data may be acquired from the patient by, for example,
imaging device 102 using techniques such as magnetic resonance (MR)
imaging, computed tomography (CT), helical CT, X-ray, angiography,
positron emission tomography (PET), fluoroscopy, ultrasound, single
photon emission computed tomography (SPECT), or a combination
thereof.
[0044] At 206, measurement tool 106 automatically measures anterior
shift of a target vertebra. The target vertebra is any vertebra
along the spine that is identified for further study. The anterior
shift generally refers to an acquired anterior displacement of the
target vertebra relative to another structure (e.g., adjacent or
subjacent vertebra).
[0045] In some implementations, measurement tool 106 automatically
measures the anterior shift by measuring the displacement of the
target vertebra relative to another anatomical structure. In
another implementation, measurement tool 106 automatically measures
the anterior shift by applying a deep learning technique to
determine centroids of the vertebrae, vertebra-center coordinate
systems or vertebra-specific landmarks, and determining the
relative displacement of a vertebra with respect to its adjacent
vertebra based on the centroids, vertebra-centers or
vertebra-specific landmarks. In yet another implementation, the
overall spine profile may be characterized (e.g. by a centerline or
deep learning), and the anterior shift (or amount of
spondylolisthesis) may be determined based on a deformation of the
overall spine profile. This is especially useful in determining the
overall shape or posture and identifying incremental changes.
[0046] In some implementations, measurement tool 106 automatically
measures the anterior shift of the target vertebra by identifying
the front (or back) planes of the target and adjacent vertebrae,
wherein the front (or back) planes are perpendicular to the plane
of the spinal disc between the vertebrae. The anterior shift may
then be determined by the distance between the front (or back)
planes. FIG. 3 illustrates an exemplary automatic measurement of
anterior shift. Three vertebrae 302a-c are shown for illustration
purposes. The measurement tool 106 may first identify the front
plane 303 of the target vertebra 302a, and the front plane 305 of
the subjacent vertebra 302b. The anterior shift 312 of one vertebra
302a over its subjacent vertebra 302b may be obtained by the
distance 312 between the front plane 303 and the front plane
305.
[0047] In yet other implementations, measurement tool 106
automatically measures the anterior shift by segmenting the target
vertebra and its adjacent (e.g., subjacent) vertebra in the image
data and determining the displacement of a point on the lower (or
upper) surface of the target vertebra relative to a point on the
upper (or lower) surface of the adjacent vertebra. To segment the
target and adjacent vertebrae, the measurement tool 106
automatically detects pre-defined key landmarks of the spine. Such
landmarks are pre-defined at key locations (e.g., center of each
vertebra) to characterize anatomic and topological information of
the spine. The landmarks may be detected by a machine
learning-based engine that has been trained offline with a set of
pre-defined training images. Based on the detected landmarks, a
region of interest (ROI) containing a vertebra of the spine may be
extracted. Accordingly, each vertebra of the spine may be segmented
without the confusion caused by its adjacent vertebrae. The
segmentation process may automatically generate semantic labels
that identify the segmented vertebrae, such as cervical, thoracic
and/or lumbar vertebra labels.
[0048] A multi-atlas segmentation scheme may also be performed. The
vertebrae in the training images may be manually segmented to build
a set of vertebral atlases for training the engine. Each vertebral
atlas is registered to the target vertebra. The transformation
model may be based on rigid, affine, or deformable transformations.
After registration, intelligent fusion methods may be applied to
derive the final segmentation results on the target and adjacent
vertebrae based on the registered atlases. The fusion method may
be, for example, a majority voting technique or a non-local
mean-based label fusion technique.
[0049] FIG. 4 illustrates another exemplary automatic measurement
of anterior shift. Three segmented vertebrae 402a-c are shown for
illustration purposes. The measurement tool 106 may first identify
the most anterior point 404 and the most posterior point 403 of the
lower surface of target vertebra 402a, and the most anterior point
406 and most posterior point 405 of the upper surface of the
subjacent vertebra 302b. The anterior shift 412 of one vertebra
402a over its subjacent vertebra 402b may be obtained by projecting
its most posterior point 403 on its lower surface to the upper
surface of its subjacent vertebra 402b, and calculating the
distance 412 between the projected point 408 and the most posterior
point 405 on the upper surface of the subjacent vertebra 402b. The
percentage of anterior shift (or slippage) may be determined by
calculating the ratio of the anterior shift distance 412 to the
distance between the most anterior point 406 and the most posterior
point 405 on the upper surface of the subjacent vertebra.
[0050] Returning to FIG. 2, at 208, measurement tool 106 determines
a degree of spondylolisthesis based on the anterior shift. In some
implementations, the degree of spondylolisthesis is classified
using the Meyerding grading system. The Meyerding grading system
quantifies the degree of spondylolithesis based on the percentage
of anterior shift of the vertebrae relative to its subjacent one
(i.e., slippage). The Meyerding grading system has four different
grades: (1) Grade 1: <25% slippage; (2) Grade 2: 25-50%
slippage; (3) Grade 3: 50-75% slippage; and (4) Grade 4: >75%
slippage. The appropriate grade may be assigned based on the shift
percentage according to the Meyerding grading system.
[0051] At 210, report generator 108 generates a report to present
the degree of spondylolisthesis. The report may be displayed at,
for example, workstation 103. The report may include the
measurements presented in an image that is anatomically aligned to
the anatomic structure, and adjusted to account for appropriate
viewing conditions. In addition, the report may be presented in
relation to a representative sub-population of patients with
similar characteristics (e.g., anterior shift, degree of
spondylolisthesis) as the patient, as will be described in more
details with reference to FIG. 10.
[0052] FIG. 5 shows exemplary medical images 501a-d illustrating
automatic measurements of anterior shift 502a-d for patients with
normal spines. Such images 501a-d may be presented in the report
generated by report generator 108 to illustrate the measurements of
anterior shift. As shown, there is no anterior shift 502a-d for the
normal spines shown in the images 501a-d.
[0053] FIG. 6 shows exemplary medical images 601a-d illustrating
automatic measurements of anterior shift 602a-d for grade 1
spondylolisthesis patients. Such images 601a-d may be presented in
the report generated by report generator 108 to illustrate the
measurements of anterior shift. As shown, the percentage of
anterior shift are 15.51%, 21.66%, 19.67% and 14.95% for images
601a, 601b, 601c and 601d respectively.
[0054] FIG. 7 shows exemplary medical images 701a-b illustrating
automatic measurements of anterior shift 702a-b for grade 2
spondylolisthesis patients. Such images 701a-b may be presented in
the report generated by report generator 108 to illustrate the
measurements of anterior shift. As shown, the percentage of
anterior shift are 27.57% and 35.47% for images 701a and 701b
respectively.
[0055] The use of automated measurement tools, in routine as well
as specialized study, allows for increase in efficiency, reduction
in inter- and intra-reader variability, and archival of
quantitative information as structured data for future analysis.
Once validated by a physician, the structured data becomes the
basis for implementing effective and efficient patient management
decisions. Automated measurement tools enable quantification of the
clinical condition or disease that is repeatable and consistent,
enabling assessment of change of the condition or disease over
time. Thus, automated anatomic measurements enables quantitative
assessments of an onset of a disease or provide a means to quantify
the growth of pathology that can be trended for the specific
patient in the context of a larger population of patients.
Automated measurements can be used by surgeons to, for example,
select proper surgical devices. Automated three-dimensional (3D)
measurement tools may also be directly provided to the surgeon,
unlike in current workflows where surgeons only have access to
two-dimensional (2D) images, from which the measurements may not
reflect the 3D truth.
[0056] FIG. 8 shows an exemplary method 800 of building a database
109 by a computer system. It should be understood that the steps of
the method 800 may be performed in the order shown or a different
order. Additional, different, or fewer steps may also be provided.
Further, the method 800 may be implemented with the system 101 of
FIG. 1, a different system, or a combination thereof.
[0057] At 804, database builder 107 receives patient data of a
population of patients. The number of patients in the sample
population may range from a few thousands to millions. The patient
data may include image data and associated anatomic measurements of
at least one structure of interest, as well as other patient
information. The image data may be acquired by, for example,
imaging device 102, using techniques such as magnetic resonance
(MR) imaging, computed tomography (CT), helical CT, X-ray,
angiography, positron emission tomography (PET), fluoroscopy,
ultrasound, single photon emission computed tomography (SPECT), or
a combination thereof. Patient information may include, but is not
limited to, patient history, examination reports, demographic
information (e.g., ethnicity), specific patient information (e.g.,
age, gender, body mass index or BMI, weight, cholesterol level,
allergy, test results, smoking, substance use, family history),
risk factors or a combination thereof. Characteristics of the
patient may be extracted from such patient information.
[0058] The anatomic measurements may one-dimensional,
two-dimensional, three-dimensional, or other dimensional. The
anatomic measurements may be performed on different structures of
interest, such as at least a portion of the spine, heart, aorta,
blood vessel, skeleton, muscle, nervous system, kidney, and so
forth. In addition, different types of measurements may be
performed. Exemplary measurements include, but are not limited to,
diameter, length, thickness, area, volume, flow, change, and/or
rate of change of measurements. For example, measurements may be
performed to determine the anterior shift of one vertebra over its
subjacent vertebra and/or the degree of spondylolisthesis, as
previously described with reference to FIG. 2. The measurements may
also be determined based on relationships between different base
measurements, including but not limited to, angles, ratios, and so
forth.
[0059] The anatomic measurements may have been performed either
manually, semi-automatically or automatically. In some
implementations, the patient data is automatically processed by
measurement tool 106 to generate all available measurement values,
such as the dimensions of bones in the body and relationships
between them, dimensions and characteristics of organs,
measurements of the complete anatomic system (e.g., respiratory,
nervous system, etc.). The anatomic measurements may be performed
based on anatomic landmarks pre-defined according to
well-established guidelines (e.g., American College of Cardiology
(ACC) and the American Heart Association (AHA) pocket guidelines
for diagnosis and management of patient with thoracic aortic
disease). Alternatively, or additionally, the anatomic measurements
may be performed along a continuum of locations throughout the
structure of interest. By supplying not only information at
specific data points, but in a continuum of locations (as
applicable), potential sources or onset of diseases may be
revealed. These sources may be missed by measuring only at the
discrete or pre-specified locations.
[0060] The anatomic measurements may be performed to, for example,
assess the state of a specific anatomic structure and determine:
whether the measurement values are within acceptable ranges
according to demographic characteristics (e.g., ethnicity) and/or
specific patient characteristics (e.g., age, gender, body mass
index, smoking, substance use, family history); whether the
measurements are impacted by risk factors originating from
environmental or working conditions; or whether the measurements
have changed since the prior examination and whether the change is
significant given the patient characteristics or risk factors.
[0061] At 806, database builder 107 clusters patient records into
sub-populations based on the anatomic measurements and/or
characteristics extracted from the patient data. The patient
records in each sub-population are more similar with respect to
anatomic measurement values and/or characteristics to each other
than those in other sub-populations thereof. Machine learning-based
techniques may be employed to cluster the patient records into
meaningful sub-populations. In some implementations, the machine
learning techniques include a deep learning algorithm based on, for
example, deep neural networks, convolutional deep neural networks,
deep belief networks and recurrent neural networks. The patient
records may be clustered according to, for example, diseases or
clinical field of interest (e.g., cardiology, neurology,
nephrology, etc.) determined by the type (or location) of the
structures of interest measured by the anatomic measurements. The
patient records may also be clustered with respect to the degree of
condition or disease (e.g., spondylolisthesis grade) or range of
values derived from the anatomic measurements. The patient records
may further be clustered with respect to characteristics (e.g.,
ethnicity, age, gender, BMI, risk factor) extracted from the
patient information.
[0062] At 808, database builder 107 associates each sub-population
with one or more treatments. The one or more treatments (or
therapies) may have been performed during prior patient management
as evidenced by the patient information associated with the
population of patients. The information of such prior treatments
may be extracted from, for example, patient history records or
examination reports associated with the patients. Exemplary
treatments include, but are not limited to, surgery, medication,
radiation, hormone replacement, ablation, chemotherapy, physical
therapy, and so forth. The mapping information that relates the
sub-populations to their respective treatments may be stored in,
for example, a table, index file or other suitable data structure
in database 109.
[0063] At 810, database builder 107 associates each treatment with
one or more outcomes. The one or more outcomes have resulted from
performing the associated treatment (or therapy) during previous
patient management. Information of such previous treatments may be
extracted from, for example, patient history. Each outcome
indicates the effectiveness of the associated treatment. For
example, the outcome may indicate that the disease or condition is
cured, in complete remission, partial remission, improved, stable
or refractory. Other types of outcomes may also be predefined. The
mapping information that relates the treatments to the outcomes may
be stored in, for example, a table, index file or other suitable
data structure stored in database 109. Outcomes can not only be
associated with individual sub-populations of patients, but can
also be related to clinical studies that provide peer-reviewed
analysis for a cohort of patients.
[0064] The creation of sub-populations and mapping of therapies to
outcomes may be informed by published literature (e.g.,
peer-reviewed publications and meta-analyses). In some
implementations, such information (e.g., mapping) is automatically
extracted by a text processing unit which parses the literature
document searching for specific information or keywords. This is an
evolution of the automation of diagnostic report reading to
understand and extract information such as therapies and outcomes.
Alternatively, the mapping information is semi-automatically or
manually incorporated by specialized experts. The use of published
literature can prove to be of substantial value from an
evidence-based medicine perspective, when associating therapies to
outcomes.
[0065] FIG. 9 shows an exemplary clustering of a collection of
patient records 902. By processing large collections of patients,
anatomic measurements may be associated with risk factors and
related to subpopulations, therapies and outcomes. As shown,
anatomic measurements 904 are derived for a large collection of
cases. The patient records associated with the anatomic
measurements 904 are clustered into sub-populations 1) to (k) with
respect to, for example, clinical area of interest and patient
characteristics. Based on patient history, sub-populations (1) to
(k) are mapped to treatments (or therapies) (1) to (n), which are
then mapped to outcomes (1) to (O).
[0066] FIG. 10 shows an exemplary method 1000 of generating a
report by a computer system. It should be understood that the steps
of the method 1000 may be performed in the order shown or a
different order. Additional, different, or fewer steps may also be
provided. Further, the method 1000 may be implemented with the
system 101 of FIG. 1, a different system, or a combination
thereof.
[0067] At 1004, report generator 108 receives patient data of a
given patient. The given patient may be, for example, a patient
currently undergoing examination, evaluation or therapy (or
treatment) by a physician. The patient data may include image data
of a structure of interest and associated anatomic measurements, as
well as other patient information (e.g., age, ethnicity, gender,
risk factor, laboratory test analysis results). The structure of
interest may be, for example, a tubular structure (e.g. vascular
structure, airway, urinary track, etc.), a bone structure (e.g.,
femur, skull, ribs, spine, etc.), nerves, etc. The image data may
be acquired by, for example, imaging device 102, using techniques
such as magnetic resonance (MR) imaging, computed tomography (CT),
helical CT, X-ray, angiography, positron emission tomography (PET),
fluoroscopy, ultrasound, single photon emission computed tomography
(SPECT), or a combination thereof.
[0068] The anatomic measurements may one-dimensional,
two-dimensional, three-dimensional, or other dimensional. The
anatomic measurements may have been performed on image data either
manually, semi-automatically or automatically. In some
implementations, the anatomic measurements are automatically
performed by measurement tool 106. For example, the measurements
may be performed to determine the degree of spondylolisthesis, as
previously described with reference to FIG. 2. It should be
appreciated that other types of measurements, such as diameter,
length, thickness, area, volume, flow, change, and/or rate of
change of measurements, may also be determined. The measurements
may also be determined based on relationships between different
measurements, including but not limited to, angles, ratios, and so
forth.
[0069] In some implementations, the anatomic measurements are
performed at pre-defined anatomic landmark locations according to
well-established guidelines. FIG. 11 show exemplary anatomic
landmark locations according to the ACC/AHA guidelines for
diagnosis and management of patients with thoracic aortic disease.
Volume-rendered CT image 1102 shows the normal anatomy of a
thoracoabdominal aorta with pre-defined anatomic landmarks 1-9 for
measuring aortic arch diameters. Table 1104 describes the
corresponding locations of the anatomic landmarks 1-9.
[0070] The anatomic measurements may also be performed at a
continuum of locations, including the pre-defined anatomic landmark
locations, throughout the structure of interest. Continuum of
measurements is of importance when measurements are performed to
capture the onset of the disease. In the context of aortic
diameters, for example, the measurements are performed to capture
the potential onset of aortic dissection. However, actual
enlargement of aorta may be manifested at a higher or different
location than where the diameter is measured according to
guidelines. For example, when measuring the mid ascending diameter
over time to detect enlargement, an actual enlargement located
between the proximal descending thoracic aorta (6) and mid
descending aorta (7) (as shown in FIG. 11) may be missed if the
diameters are measured only at positions (6) and (7). In addition
to the measurements at the prescribed locations, a diameter profile
for the whole length of the aorta highlighting areas which are out
of bounds from normal diameter may be presented to the physician to
facilitate early identification of the onset of disease.
[0071] Returning to FIG. 10, at 1006, report generator 108
determines a sub-population of patient records from the database
109 that best represents the patient. The database 109 may have
been previously clustered into pre-defined sub-populations by
database builder 107, as previously described with reference to
FIG. 8. Atlases may be created for specific sub-populations to
capture the underlying characteristics of the cluster and making it
easier to relate a measurement for a given patient to the whole
sub-population. The representative sub-population may be selected
by finding the sub-population that is most similar to the given
patient by matching patient characteristics and anatomic
measurements extracted from the patient data with those associated
with each sub-population.
[0072] At 1008, report generator 108 generates a report that
presents the anatomic measurements in relation to the
representative sub-population. In some implementations, report
generator 108 automatically associates the patient's anatomic
measurements with corresponding labeled anatomic measurements
extracted from the patient records in the representative
sub-population. Report generator 108 may compare the patient's
measurements with the extracted measurements to identify
differences between the patient and the sub-population and present
such differences in the report. The patient's measurements may also
be compared with each other to identify any lack of symmetry,
anomaly or difference from other similar anatomic structures within
the patient's body (e.g. vertebral height, vertebral listhesis,
vascular diameter, etc.). Any differences or anomalies may be
manifested or highlighted in the report to facilitate discovery of
a latent form or possible early onset of a clinical condition or
disease in patients who may be asymptomatic for that particular
condition or disease (e.g., vertebral listhesis, aortic dissection,
etc.). Additionally, anomalies may be automatically correlated with
medical publications or knowledge to enable evidence-based
identification of a condition or disease.
[0073] In some implementations, report generator 108 presents a
summary view that represents the anatomic measurements at
pre-defined anatomic landmarks in a concise form. FIG. 12
illustrates an exemplary chart 1202 in a summary view. Average
aortic diameter values measured at various anatomic landmark
locations 1-9 are connected by the solid line 1204. Vertical error
bars 1208 represent the maximum and minimum measurement values at
each location for the population representative of the patient.
Average aortic diameter values associated with the sub-population
representative of the given patient are connected by the dotted
line 1206.
[0074] It can be observed that most of the diameters 1204 are close
to the average diameters 1206 of the representative sub-population,
the diameters at locations 8 and 9 are of clinical concern as they
are much closer to the upper bounds 1212 of the measurement
variation for the representative sub-population. Hence, while the
physician may likely want to monitor all measurement values over
time, he or she may pay particular attention not only to
measurements at locations 8 and 9, but also in the aorta area,
since the measurement values are proxies for the actual aortic
diameters for capturing the underlying onset of the disease. An
"alarm" mechanism may be provided to generate an alert when
measurements exceed the norm (or threshold) or are too close to a
boundary. This may help in making sure that these events are noted.
The mechanism may be automated to facilitate processing of hundreds
of measurements.
[0075] The risk of dissection may be related to the diameter of the
aorta. See, for example, Erbel R, Eggebrecht H, "Aortic dimensions
and the risk of dissection," Heart, 2006;92(1):137-142, which is
herein incorporated by reference. In some implementations, report
generator 108 presents the measurements in relation to associated
risk levels. FIG. 13 illustrates the influence of aortic size on
cumulative and lifetime incidences of natural complications of
aortic aneurysm. Exemplary graphs 1302 and 1304 are shown to
illustrate the risk of complication at the mid-ascending and
mid-descending respectively. Each graph (1302, 1304) plots the
risks of complication (in percentage) for aortic dissection against
the average diameter (in centimeters) at a given location for a
particular individual. The age of the patient is accounted for in
the computation of risk. The risk level for the current patient is
exemplified by the dot (1303, 1305).
[0076] Report generator 108 may also generate a report that relates
anatomic measurements to patient-specific characteristics (e.g.,
age, gender, body surface area or BSA, etc.) of the given patient
and the representative sub-population to facilitate identification
of observable trends following subsequent examination. FIG. 14a
shows a chart 1402 depicting measurements of aortic diameter (in
millimeters) as a function of BSA. The measurements are made with
consideration of Gender (F: female or M: male), at Age <45 yrs
and 4 locations (A: ascending or D: descending). See, for example,
WANG Y-L, WANG Q-L, WANG L, et al., "Body surface area as a key
determinant of aortic root and arch dimensions in a
population-based study," Experimental and Therapeutic Medicine.
2013;5(2):406-410; and Pearce et al., "Aortic diameter as a
function of age, gender, and body surface area," Surgery, 1993
October;114(4):691-7, which are all herein incorporated by
reference. More particularly, the solid lines (F(AA) and M(AA))
represent the female and male functions at the AA location
respectively, while the broken lines (F(AD) and M(AD)) represent
the female and male functions at the AD location respectively. The
points denoted by Subj(AA) and Subj(AD) represent the relative
measurements of aortic diameters as a function of BSA for a given
patient measured at two locations (AA and AD) tracked over time
(t1, t2, t3, t4).
[0077] FIG. 14b shows a chart 1412 depicting measurements of aortic
diameter (in millimeters) as a function of BSA at the ascending
location for male patients (M) with consideration for different age
groups (<45, 45-54, 55-64, >64). The points denoted by Subj 1
and Subj 2 represent the relative measurements of aortic diameters
(at a given location AA) as a function of BSA for two individual
patients tracked over time (t1, t2, t3, t4).
[0078] Measurements may change not only due to pathology, but also
as a byproduct of aging. See, for example, O'Rourke M, Farnsworth
A, O'Rourke J. Aortic, "Dimensions and Stiffness in Normal Adults,"
J Am Coll Cardiol Img, 2008;1(6):749-751, which is herein
incorporated by reference. For example, the ascending aorta of a
20-year-old patient may be compared with the ascending aorta of an
80-year-old man. The length of the ascending aorta may increase by
approximately 12% per decade, and the diameter may increase by 3%
per decade. By considering specific patient characteristics (e.g.,
age), the information extracted and summarized for a sub-population
may be used to account for anatomic changes associated with
aging.
[0079] At 1010, report generator 108 generates a report that
presents one or more recommendations for treatment (or therapy)
associated with the representative sub-population. Report generator
108 may present treatment options with the best outcomes associated
with the particular sub-population representative of the given
patient by using the mapping information relating the
sub-population to the treatment and associated outcomes (as
previously described with reference to FIG. 8). Effectiveness of
treatment (or therapy) may be advantageously assessed at an earlier
stage by being able to identify the given patient within a
sub-population. The individual risk associated with the given
patient may also be determined.
[0080] Additionally, report generator 108 may extract
recommendations of the most suitable treatment given the
measurements from published peer-reviewed journal literature and/or
clinical data. Such publications may be stored in, for example,
database 109 or additional databases, and mapped to one or more
specific sub-populations to be available as evidence-based
documentation accessible to a physician when determining the
therapy plan.
[0081] In some implementations, the report containing measurements
and/or recommendations for treatment is presented in a dashboard or
dashboard-like environment for performing informed patient
management based on evidence. The report may be presented at, for
example, workstation 103. The dashboard may allow the physician to
explore various therapy or treatment scenarios by modifying the
patient's characteristics (e.g. to simulate the event whereby the
patient reduces cholesterol or weight), or by simulating positive
or negative responses to a drug (e.g., side effects which are
presently unknown for a specific patient). The dashboard may
further enable input of intermediary results from the treatment
plan and present the impact on the possible outcome for the
particular patient.
[0082] The dashboard may enable an interactive workflow that allows
an efficient review of anomalous anatomy with potential indication
of an associated condition or disease. The dashboard may enable the
user to not only review individual measurements relative to a
sub-population, but also to define relevance thresholds for
filtering the measurements. For example, when reviewing a complete
spine for spondylolisthesis, the user may request to see only
measurement values and changes that are above a certain tolerance
value, e.g. 5% or 3 mm. This may greatly simplify the review
process and not force the user to review every single vertebra
along the spine.
[0083] The dashboard may further notify or alert the physician of a
change in the disease or condition (e.g., worsened, stabilized or
improved) subject or not-subject to therapy. An overall view of all
the measurements may be presented using color coding or other
visual mapping to indicate changes (e.g., blue indicates no change;
red indicates significant change). The relevance of the change may
be derived from published literature or institution-specific
guidelines and presented on the dashboard. The change in condition
may be considered with respect to other comorbidities or conditions
in the patient (or other similar patients in the representative
sub-population) and may contribute as a risk factor for the current
or other disease. For example, quantification of change in plaque
in the aorta, blood pressure, level of cholesterol or calcium
scoring in the coronaries, may be used to determine a change in the
risk of heart attack occurrence.
[0084] The patient may be provided access to the dashboard to view
his or her own health status, and/or update or upload information
from a mobile device or network connected device to monitor blood
pressure, weight, sugar level, or other assay available to
commercially available platforms. The patient or physician may also
link other members of the patient's family to provide input to
enhance the patient's profile, thus enabling better tailoring of
therapy and refinement of subpopulation characterization.
[0085] Recommendations for treatments may be filtered based on the
medical cost payer's (e.g., insurance company's) reimbursement
plans. While a patient may undergo multiple treatment plans to
reach the same outcome or various outcomes, payers may endorse only
some of the available therapies. Based on the patient-specific
insurance coverage, for example, the dashboard may present only
treatments that are covered by the reimbursement plan of a medical
cost payer (e.g., insurance plan).
[0086] While the present framework has been described in detail
with reference to exemplary embodiments, those skilled in the art
will appreciate that various modifications and substitutions can be
made thereto without departing from the spirit and scope of the
invention as set forth in the appended claims. For example,
elements and/or features of different exemplary embodiments may be
combined with each other and/or substituted for each other within
the scope of this disclosure and appended claims.
* * * * *