U.S. patent application number 15/311692 was filed with the patent office on 2017-03-30 for inference transparency system for image-based clinical decision support systems.
The applicant listed for this patent is Brainlab AG. Invention is credited to Balint Varkuti.
Application Number | 20170091386 15/311692 |
Document ID | / |
Family ID | 51564623 |
Filed Date | 2017-03-30 |
United States Patent
Application |
20170091386 |
Kind Code |
A1 |
Varkuti; Balint |
March 30, 2017 |
Inference Transparency System for Image-Based Clinical Decision
Support Systems
Abstract
A medical data processing method for supporting determination of
medical image data describing a spatial distribution of body tissue
which is the subject of a medical procedure, the method comprising
the following steps which are constituted to be executed by a
computer: a) acquiring outcome atlas data describing a general
probability that a specific anatomical structure can be treated
successfully by means of the medical procedure, the general
probability having been determined based on a statistical analysis
of medical image data and/or treatment effect simulations conducted
on said medical image data generated from a general population of
human bodies; b) acquiring general population data describing at
least one statistical feature of each member of the general
population; c) acquiring patient definition data describing at
least one statistical feature of a patient who shall become the
subject of the medical procedure; d) determining, based on the
outcome atlas data and the general population data and the patient
definition data, adapted population data describing an adapted
population of human bodies taken from the general population,
wherein the adapted population is to be used as a basis for
generating adapted outcome atlas data describing an adapted
probability that the specific anatomical structure can be treated
successfully by means of the medical procedure, which adapted
probability is determined based on the adapted population.
Inventors: |
Varkuti; Balint; (Munich,
DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Brainlab AG |
Feldkirchen |
|
DE |
|
|
Family ID: |
51564623 |
Appl. No.: |
15/311692 |
Filed: |
September 5, 2014 |
PCT Filed: |
September 5, 2014 |
PCT NO: |
PCT/EP2014/068938 |
371 Date: |
November 16, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/EP2014/001330 |
May 16, 2014 |
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15311692 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 19/324 20130101;
G16H 40/63 20180101; G16H 30/40 20180101; G06F 19/325 20130101;
G16H 50/20 20180101; G06F 19/321 20130101; G16H 20/40 20180101;
G16H 50/70 20180101; G16H 40/20 20180101 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Foreign Application Data
Date |
Code |
Application Number |
May 16, 2014 |
EP |
PCT/EP2014/001330 |
Claims
1.-15. (canceled)
16. An image processing system, comprising: at least one display
device; at least one processor; memory coupled to the at least one
processor, wherein the memory stores instructions that when
executed by the at least one processor cause the at least one
processor to: acquire, at the at least one processor, outcome atlas
data describing a general probability that a specific anatomical
structure can be treated successfully by a medical procedure, the
general probability based on analysis of medical image data
conducted on said medical image data generated from a general
population of human bodies; wherein the medical image data
describes a spatial distribution of body tissue which is the
subject of the medical procedure; acquire, at the at least one
processor, general population data describing at least one
statistical feature of each member of the general population;
acquire, at the at least one processor, patient definition data
describing at least one statistical feature of a patient who is the
subject of the medical procedure; determine, by the at least one
processor and based on the outcome atlas data and the general
population data and the patient definition data, adapted population
data describing an adapted population of human bodies taken from
the general population; the adapted population data determined
based on a degree of similarity entered by a user input through a
graphical user interface on the display device of a slider
illustrating the relationship between the similarity between the
general population and the patient, and the number of similar
members of the general population; wherein the adapted population
data is used for generating adapted outcome data describing an
adapted probability that the specific anatomical structure can be
treated successfully by the medical procedure; wherein the specific
anatomical structure includes tumour tissue and the general
probability and the adapted probability each describe a probability
that the tumour tissue can be successfully treated; the outcome
atlas data including medical image data from which it has been
generated and wherein the adapted population data is obtained from
comparing the patient medical image data to the outcome atlas data
through an image fusion; determine, by the at least one processor
and based on the adapted outcome data and the patient medical image
data, alteration probability data describing a probability of
successfully altering the specific anatomical structure; determine,
by the at least one processor and based on the alteration
probability data and the patient medical image data, alteration
probability display data for displaying graphical information
describing the probability of successfully altering the specific
anatomical structure simultaneously with the patient medical image
data; display, on the display, based on the user input of the
graphical user interface slider for determining the user input to
adjust the adapted population data, the alteration probability
display data describing the probability of successfully treating
the specific anatomical structure simultaneously along with
displaying the patient medical image data.
17. A computer-implemented image processing method for supporting
determination of medical image data describing a spatial
distribution of body tissue which is the subject of a medical
procedure implemented by at least one processor, comprising:
acquiring, at the at least one processor, outcome atlas data
describing a general probability that a specific anatomical
structure can be treated successfully by the medical procedure;
wherein the general probability is determined on a statistical
analysis of medical image data generated from a general population
of human bodies; acquiring, at the at least one processor, general
population data describing at least one statistical feature of each
member of the general population; acquiring, at the at least one
processor, patient definition data describing at least one
statistical feature of a subject patient of the medical procedure;
determining, by the at least one processor and based on the outcome
atlas data and the general population data and the patient
definition data, adapted population data describing an adapted
population of human bodies taken from the general population;
generating adapted outcome atlas data based on the adapted
population, the adapted outcome atlas data describing an adapted
probability that the specific anatomical structure can be treated
successfully by means of the medical procedure, which adapted
probability is determined based on the adapted population;
displaying the generated adapted outcome atlas data on a
display.
18. The method according to claim 17, comprising: determining, by
the at least one processor and based on the outcome atlas data and
the adapted population data, adapted outcome data describing a
probability with which the specific anatomical structure can be
successfully altered within a body of a patient; acquiring, at the
at least one processor, patient medical image data describing a
medical image of the anatomical structure in the body of the
patient; determining, by the at least one processor and based on
the adapted outcome data and the patient medical image data,
alteration probability data describing a probability of
successfully altering the anatomical structure; determining, by the
at least one processor and based on the alteration probability data
and the patient medical image data, alteration probability display
data; displaying the alteration probability display data
simultaneously with the patient medical image data on the display
in graphical information describing the probability of successfully
altering the anatomical structure.
19. The method according to claim 18, wherein the adapted outcome
data is generated on the basis of medical image data so that the
adapted outcome data can be displayed as a statistical map
simultaneously with the medical image data.
20. The method according to claim 17, wherein the adapted
population data is analysed, by the at least one processor, to
infer a contrast-to-noise-property which describes the minimum size
of a subset of the adapted population data that carries statistical
significance with respect to an available property.
21. The method according to claim 20, wherein the available
property is the outcome of carrying out the medical procedure on
the patient.
22. The method according to claim 17, wherein the adapted
population is a selection from the general population.
23. The method according to claim 17, wherein the at least one
statistical feature of the patient can be compared to the at least
one statistical feature of each member of the general population,
and wherein the adapted population data is determined by comparing
the value of the at least one statistical feature of the patient to
the value of the at least one statistical feature of each member of
the general population, and further including: determining, by the
at least one processor and as the members of the adapted population
and on the basis of the result of the comparison of the values of
the at least one statistical feature, members of the general
population which have a predetermined similarity to the
patient.
24. The method according to claim 17, wherein the adapted
population data is determined by the at least one processor, based
on user input of a predetermined degree of similarity between the
members of the general population and the patient.
25. The method according to claim 24, wherein the user input is a
graphical user input of a representation of a slider in a
statistical diagram illustrating the relationship between the
similarity between the general population and the patient, and the
number of similar members of the general population.
26. The method according to claim 17, wherein the at least one
statistical feature of each member of the general population and
the at least one statistical feature of the patient pertain to at
least one of genetic information and information about the gender,
age, ethnicity, mass and pathological state of the members of the
general population and the patient, respectively.
27. The method according to claim 17, wherein the anatomical
structure comprises tumour tissue and wherein the general
probability and the adapted probability each describe a probability
that the tumour tissue can be successfully resected.
28. The method according to claim 17, comprising: acquiring patient
medical image data describing a medical image of the anatomical
structure in the body of the patient, wherein the adapted
population data is determined further based on the patient medical
image data.
29. The method according to claim 28, wherein the outcome atlas
data comprises the medical image data from which it has been
generated, and wherein determining the adapted population data
comprises comparing the patient medical image data to the outcome
atlas data.
30. The method according to claim 29, wherein the comparing the
patient medical image data to the outcome atlas data comprises
selecting the adapted population such that the members of the
adapted population are associated with medical image data having a
predetermined degree of similarity to the patient medical image
data.
31. The method according to claim 29, wherein comparing the patient
medical image data to the outcome atlas data comprises running an
image fusion algorithm on the patient medical image data and the
outcome atlas data.
32. The method according to claim 28, comprising: determining, by
the at least one processor, patient feature vector data comprising
the patient definition data and the result of an analysis of the
patient medical image data with regard to morphological traits of
the patient's anatomy; determining, by the at least one processor,
the adapted population data further based on the patient feature
vector data.
33. A computer implemented image processing method for supporting
determination of medical image data describing a spatial
distribution of body tissue which is the subject of a medical
procedure, comprising: acquiring by at least one processor, outcome
atlas data describing a general probability that a specific
anatomical structure can be treated successfully by means of the
medical procedure, wherein the general probability having been
determined based on a statistical analysis of medical image data
generated from a general population of human bodies; acquiring, by
at least one processor, general population data describing at least
one statistical feature of each member of the general population;
acquiring, by at least one processor, patient definition data
describing at least one statistical feature of a patient subject to
the medical procedure; determining, by at least one processor and
based on the outcome atlas data and the general population data and
the patient definition data, adapted population data describing an
adapted population of human bodies taken from the general
population; wherein the adapted population is used as a basis for
generating adapted outcome atlas data describing an adapted
probability that the specific anatomical structure can be treated
successfully by means of the medical procedure, which adapted
probability is determined based on the adapted population;
displaying on a display by at least one processor, the adapted
probability.
Description
[0001] The present invention is directed to a medical data
processing method in accordance with claim 1 for supporting
determination of medical image data describing a spatial
distribution of body tissue, a corresponding computer program, a
computer running that program and a radiotherapy system or
radiosurgery system comprising that computer.
[0002] Clinical Decision Support Systems which are image-based can
be grouped in the following fashion: [0003] Treatment effect
systems for implantation (bringing objects, substances or radiation
into the body) or excision (eliminating existing objects such as
tumours). While effects of implantation systems are not necessarily
always image-verified (except for the deviation of planned
trajectory/target and actually reached trajectory/target precision
or changes in tissue composition/volume resulting from a radiation
treatment) but have health effects mainly observable in patient
metainformation (e.g. symptom reduction, increase in quality of
life), the outcome of excision treatments can be directly measured
on an image basis since post-intervention images in general show
the volume reduction of the target object (e.g. tumour shrinking)
or volume replacement (replacing tumour volume with a resection
cavity/replacement tissue). [0004] Clinical decision support
systems offer decision support for the clinician through inferences
that are drawn from the presented case data (e.g. medical records)
and an amount of input data that serves as the decision basis (e.g.
loaded medical literature). In the case of structured data (e.g.
database entries) or unstructured data (e.g. medical reports in
text form) both are analyzed and weighted to come to a decision or
a ranked list of suggestions, subsequently this inference is
presented to the clinician. Medical decisions are not taken,
though, because a "black box" says it would be a good idea, the
reasoning behind the taken inference needs to be transparent. While
for the sketched systems such views to illustrate the underlying
reasoning already exist (e.g. listing all relevant medical
literature reference in support of a said decision), outcome
atlases that locate and relate statistical treatment outcome
indicators in a patient population with a certain effect location
in the body (e.g. where in the treatment something was implanted or
some tissue excised) as well as the anatomical location and extent
of the treated abnormality (e.g. tumour) do not yet have one
accepted processing/viewing format to bring transparency to
inferences drawn from such mass analysis of imaging data. The
proposed invention aims to solve that problem by processing the
data in a form that can be visualized at the point of inference
giving/decision support to aid the clinician in accepting or
declining the proposed decision support.
[0005] The following publications contain teachings from the state
of the art: [0006] WO2012054612 A1 [0007] WO2013043132 A1 [0008]
WO2013117658 A1 [0009] WO2004096018 A2 [0010] WO2013123085 A1
[0011] WO2013130234 A1 [0012] P. C. De Witt Hamer, J. Hendriks, E.
Mandonnet, F. Barkhof, A. H. Zwinderman, H. Duffau, Resection
Probability Maps for Quality Assessment of Glioma Surgery without
Brain Location Bias, PLOS One, vol. 8 (2013), issue no. 9, Sep. 6,
2013
[0013] Advantages, advantageous features, advantageous embodiments
and advantageous aspects of the present invention are disclosed in
the following. Different advantageous features can be combined in
accordance with the invention wherever technically expedient and
feasible. Specifically, a feature of one embodiment which has the
same or a similar function to another feature of another embodiment
can be exchanged with said other feature, and a feature of one
embodiment which adds an additional function to another embodiment
can for example be added to said other embodiment.
[0014] The present invention has for example application in
image-guided surgery workflows where suggestions on a certain
treatment course are to be weighted in the context of the present
knowledge describing the success rate of such treatments in the
past. Further applications can be considered for utilization in
stereotaxy planning software (e.g. Trajectory Element or iPlan
Stereotaxy) by Brainlab AG where optimal implantation treatments
are planned on the basis of information from past successful
implantations, radiation treatment planning software (e.g.
Multi-Mets Element) and software designed to optimize the planning
of vascular interventions (AngioBrush Element) such as AVMs
(arterio-venous malformations).
[0015] Exemplary Short Description of the Present Invention
[0016] In the following, a short description of the specific
features of the present invention is given which shall not be
understood to limit the invention only to the features or a
combination of the features described in this section.
[0017] The present invention relates in one example aspect to a
method for determining a suitable sample of sample patients for
generating a patient-specific outcome atlas which describes the
probability of success of treating a pathologic state by resection
of tumour tissue or implanting a structure into a patient's body.
The sample patients are determined by searching a database for an
outcome atlas which is suitable in consideration of the patient's
pathologic state, and the sample patients are selected from those
which form a basis of the outcome atlas. The selection is carried
out by comparing properties of the sample patients to corresponding
properties of the patient. Sample patients which are associated
with properties fulfilling a predetermined similarity criterion
with regard to those properties of the patient are then selected as
members of an adapted population, on the basis of which a
patient-specific outcome atlas can--in one aspect of the
invention--be generated. As part of the invention, a user can
adjust the size of the sample population and thereby the degree of
similarity (i.e. the predetermined similarity criterion) required
for the sample patients to be selected as members of the adapted
population.
[0018] General Description of the Present Invention
[0019] In this section, a description of the general, in some cases
particular preferred, features of the present invention is
given.
[0020] In a first aspect, the invention relates to a method which
is a data processing method, such as for example a medical data
processing method, suitable for supporting determination of medical
image data describing a spatial distribution of body tissue which
is the subject of a medical procedure. The method for example
comprises the following steps which are for instance constituted to
be executed by a computer (further particularly, all of the steps
of the method in accordance with the invention are constituted to
be executed by a computer).
[0021] In a first exemplary embodiment, the disclosed method
comprises acquiring outcome atlas data. The outcome atlas data
describes (for example, defines) a general probability that a
specific anatomical structure can (successfully) be the subject of
the medical procedure. Being the subject of the medical procedure
means for example being treated by means (e.g. by application) of
the medical procedure. The treatment comprises for example an
alteration (for example, a physical alteration) of the anatomical
structure, such as at least one of for example removing (i.e.
resecting) body tissue from the anatomical structure or from a body
region adjacent (for example, directly neighbouring) the anatomical
region, and supplementing the anatomical structure by e.g.
conducting an implantation, such as implanting an implant into the
anatomical structure or a body region adjacent (for example,
directly neighbouring) the anatomical region. The general
probability has been determined based on for example a statistical
analysis of medical image data generated from a general population
of human bodies. Alternatively or additionally, the medical image
data can be generated based on (i.e. from) treatment effect
simulations conducted on said medical image data. The general
population for example suffers from the same pathological state
(e.g. disease or injury) which is for example the same as the
pathological state from which the patient is suffering. This allows
deriving a statement for the probability of a specific outcome
(e.g. quality of life, probability of complete healing, occurrence
of collateral damage due to application of the medical procedure
etc.) for the patient which is associated with (for example due to)
carrying out the medical procedure on the patient (for example on
the anatomical structure).
[0022] The general population is described (for example defined)
for example by general population data describing at least one
statistical feature of each member of the general population. In
one exemplary embodiment, the general population data is also
acquired by the disclosed method. The general population data
serves as metadata for the outcome atlas data and describes for
example at least one of the number of members of the general
population, their respective pathological state, the way in which
they were treated by application of a medical procedure, their age,
gender, ethnicity, genetic information, body mass and body
size.
[0023] In the first exemplary embodiment, the disclosed method
comprises for example acquiring patient definition data describing
at least one statistical feature of a patient who shall become the
subject of the medical procedure. The statistical feature is for
example at least one of the patient's pathological state, the way
in which that state were treated by application of a medical
procedure, the patient's age, gender, ethnicity, genetic
information, body mass and body size.
[0024] In the first exemplary embodiment, the method comprises for
example determining, based on the outcome atlas data and the
general population data and the patient definition data, adapted
population data. The adapted population data describes (for example
defines) an adapted population of human bodies taken from the
general population. The adapted population is for example a
selection from the general population, i.e. the adapted population
consists (only) of members of the general population, wherein it
may comprise all of the members of the general population or less
than the total of members of the general population. The adapted
population is to be used (for example in future and not necessarily
within the execution of the disclosed method) as a basis for
generating adapted outcome atlas data. The adapted outcome atlas
data describes (for example defines) an adapted probability that
the specific anatomical structure can be treated successfully by
means of the medical procedure, which adapted probability is
determined based on the adapted population.
[0025] For example, the at least one statistical feature of the
patient can be compared to the at least one statistical feature of
each member of the general population, for example the statistical
features are from the same category, i.e. describe comparable
information. The adapted population data is determined for example
by comparing the value of the at least one statistical feature of
the patient to the value of the at least one statistical feature of
each member of the general population. For example, the disclosed
method comprises determining, on the basis of the result of the
comparison of the values of the at least one statistical feature,
members of the general population which have a predetermined
similarity to the patient as the members of the adapted population.
As a further example, the adapted population data is determined
based on user input of for example a predetermined degree of
similarity between the members of the general population and the
patient. The user input is for instance effected by operating a
graphical user input such as a representation of a slider which is
displayed for example in a statistical diagram illustrating the
relationship between the similarity between the general population
and the patient on the one hand, and the number of similar members
of the general population on the other hand.
[0026] For example, the anatomical structure comprises tumour
tissue and the general probability and the adapted probability each
describe a probability that the tumour tissue can be successfully
treated, for instance resected.
[0027] In a second exemplary embodiment, the disclosed method
comprises for example at least one of the following features:
[0028] determining, based on the outcome atlas data and the adapted
population data, adapted outcome data describing (for example,
defining) a probability with which the specific anatomical
structure can be successfully altered within a body of the patient;
[0029] acquiring patient medical image data describing (for
example, defining) a medical image of the anatomical structure in
the body of the patient; [0030] determining, based on the adapted
outcome data and the patient medical image data, alteration
probability data describing (for example, defining) a probability
of successfully altering the anatomical structure; [0031]
determining, based on the alteration probability data and the
patient medical image data, alteration probability display data for
displaying graphical information describing (for example, defining)
the probability of successfully altering the anatomical structure
simultaneously (i.e. at the same point in time on the same display
apparatus or different display apparatus) with the patient medical
image data.
[0032] In an optional variation of the second exemplary embodiment,
the adapted outcome data is for example generated on the basis of
medical image data so that the adapted outcome data can be
displayed as a statistical map simultaneously with the medical
image data.
[0033] In a third exemplary embodiment, the adapted population data
is analysed to infer a contrast-to-noise-property which describes
the minimum size of a subset of the adapted population data that
carries statistical significance with respect to an available
property, for example the outcome of carrying out the medical
procedure on the patient.
[0034] In a fourth exemplary embodiment, the adapted population
data is for example analysed to infer a contrast-to-noise-property
which describes the minimum size (i.e. number of elements) of a
subset of the adapted population data that carries statistical
significance with respect to an available property, for example
with respect to the outcome of carrying out the medical procedure
on the patient.
[0035] In a fifth exemplary embodiment, the disclosed method
comprises for example at least one of the following features:
[0036] acquiring patient medical image data describing a medical
image of the anatomical structure in the body of the patient;
[0037] determining the adapted population data is determined
further based on the patient medical image data.
[0038] In a first optional variation of the fifth embodiment, the
outcome atlas data comprises the medical image data from which it
has been generated, and wherein determining the adapted population
data comprises comparing the patient medical image data to the
outcome atlas data, for example selecting the adapted population
such that the members of the adapted population are associated with
medical image data having a predetermined degree of similarity to
the patient medical image data. For example, comparing the patient
medical image data to the outcome atlas data comprises running an
image fusion algorithm on the patient medical image data and the
outcome atlas data.
[0039] In a second optional variation of the fifth embodiment, the
disclosed method comprises at least one of the following features:
[0040] determining patient feature vector data comprising the
patient definition data and the result of an analysis of the
patient medical image data with regard to morphological traits of
the patient's anatomy; [0041] determining the adapted population
data further based on the patient feature vector data.
[0042] The term of vector data denotes for example a vector-type
data structure in which the associated information is present
and/or stored.
[0043] In a second aspect, the invention is also directed to a
program which, when running on a computer, causes the computer to
perform one or more or all of the method steps described herein
and/or to a program storage medium on which the program is stored
(for example in a non-transitory form) and/or to a computer
comprising said program storage medium and/or to a (physical, for
example electrical, for example technically generated) signal wave,
for example a digital signal wave, carrying information which
represents the program, for example the aforementioned program,
which for example comprises code means which are adapted to perform
any or all of the method steps described herein.
[0044] It is within the scope of the present invention to combine
one or more features of one or more embodiments or aspects of the
invention in order to form a new embodiment wherever this is
technically expedient and/or feasible. Specifically, a feature of
one embodiment which has the same or a similar function to another
feature of another embodiment can be exchanged with said other
feature, and a feature of one embodiment which adds an additional
function to another embodiment can for example be added to said
other embodiment.
[0045] The present invention in one example does not involve or for
example comprise or encompass an invasive step which would
represent a substantial physical interference with the body
requiring professional medical expertise to be carried out and
entailing a substantial health risk even when carried out with the
required professional care and expertise. For example, the
invention does not comprise a step of positioning a medical implant
in order to fasten it to an anatomical structure or a step of
fastening the medical implant to the anatomical structure or a step
of preparing the anatomical structure for being fastened to the
medical implant. More particularly, the invention does not involve
or for example comprise or encompass any surgical or therapeutic
activity. No surgical or therapeutic step is necessitated or
implied by carrying out the invention.
DEFINITIONS
[0046] In this section, definitions for specific terminology used
in this disclosure are offered which also form part of the present
disclosure.
[0047] Within the framework of the invention, computer program
elements can be embodied by hardware and/or software (this includes
firmware, resident software, micro-code, etc.). Within the
framework of the invention, computer program elements can take the
form of a computer program product which can be embodied by a
computer-usable, for example computer-readable data storage medium
comprising computer-usable, for example computer-readable program
instructions, "code" or a "computer program" embodied in said data
storage medium for use on or in connection with the
instruction-executing system. Such a system can be a computer; a
computer can be a data processing device comprising means for
executing the computer program elements and/or the program in
accordance with the invention, for example a data processing device
comprising a digital processor (central processing unit or CPU)
which executes the computer program elements, and optionally a
volatile memory (for example a random access memory or RAM) for
storing data used for and/or produced by executing the computer
program elements. Within the framework of the present invention, a
computer-usable, for example computer-readable data storage medium
can be any data storage medium which can include, store,
communicate, propagate or transport the program for use on or in
connection with the instruction-executing system, apparatus or
device. The computer-usable, for example computer-readable data
storage medium can for example be, but is not limited to, an
electronic, magnetic, optical, electromagnetic, infrared or
semiconductor system, apparatus or device or a medium of
propagation such as for example the Internet. The computer-usable
or computer-readable data storage medium could even for example be
paper or another suitable medium onto which the program is printed,
since the program could be electronically captured, for example by
optically scanning the paper or other suitable medium, and then
compiled, interpreted or otherwise processed in a suitable manner.
The data storage medium is for example a non-volatile data storage
medium. The computer program product and any software and/or
hardware described here form the various means for performing the
functions of the invention in the example embodiments. The computer
and/or data processing device can for example include a guidance
information device which includes means for outputting guidance
information. The guidance information can be outputted, for example
to a user, visually by a visual indicating means (for example, a
monitor and/or a lamp) and/or acoustically by an acoustic
indicating means (for example, a loudspeaker and/or a digital
speech output device) and/or tactilely by a tactile indicating
means (for example, a vibrating element or a vibration element
incorporated into an instrument). For the purpose of this document,
a computer is a technical computer which for example comprises
technical, for example tangible components, for example mechanical
and/or electronic components. Any device mentioned as such in this
document is a technical and for example tangible device.
[0048] The method in accordance with the invention is for example a
data processing method. The data processing method is for example
performed using technical means, for example a computer. The data
processing method is for example constituted to be executed by or
on a computer and for example is executed by or on the computer.
For example, all the steps or merely some of the steps (i.e. less
than the total number of steps) of the method in accordance with
the invention can be executed by a computer. The computer for
example comprises a processor and a memory in order to process the
data, for example electronically and/or optically. The calculating
steps described are for example performed by a computer.
Determining steps or calculating steps are for example steps of
determining data within the framework of the technical data
processing method, for example within the framework of a program. A
computer is for example any kind of data processing device, for
example electronic data processing device. A computer can be a
device which is generally thought of as such, for example desktop
PCs, notebooks, netbooks, etc., but can also be any programmable
apparatus, such as for example a mobile phone or an embedded
processor. A computer can for example comprise a system (network)
of "sub-computers", wherein each sub-computer represents a computer
in its own right. The term "computer" includes a cloud computer,
for example a cloud server. The term "cloud computer" includes a
cloud computer system which for example comprises a system of at
least one cloud computer and for example a plurality of operatively
interconnected cloud computers such as a server farm. Such a cloud
computer is for example connected to a wide area network such as
the world wide web (WWW) and located in a so-called cloud of
computers which are all connected to the world wide web. Such an
infrastructure is used for "cloud computing", which describes
computation, software, data access and storage services which do
not require the end user to know the physical location and/or
configuration of the computer delivering a specific service. For
example, the term "cloud" is used in this respect as a metaphor for
the Internet (world wide web). For example, the cloud provides
computing infrastructure as a service (IaaS). The cloud computer
can function as a virtual host for an operating system and/or data
processing application which is used to execute the method of the
invention. The cloud computer is for example an elastic compute
cloud (EC2) as provided by Amazon Web Services.TM.. A computer for
example comprises interfaces in order to receive or output data
and/or perform an analogue-to-digital conversion. The data are for
example data which represent physical properties and/or which are
generated from technical signals. The technical signals are for
example generated by means of (technical) detection devices (such
as for example devices for detecting marker devices) and/or
(technical) analytical devices (such as for example devices for
performing imaging methods), wherein the technical signals are for
example electrical or optical signals. The technical signals for
example represent the data received or outputted by the computer.
The computer is for example operatively coupled to a display device
which allows information outputted by the computer to be displayed,
for example to a user. One example of a display device is an
augmented reality device (also referred to as augmented reality
glasses) which can be used as "goggles" for navigating. A specific
example of such augmented reality glasses is Google Glass (a
trademark of Google, Inc.). An augmented reality device can be used
both to input information into the computer by user interaction and
to display information outputted by the computer. Another example
of a display device would be a standard computer monitor comprising
for example a liquid crystal display operatively coupled to the
computer for receiving display control data from the computer for
generating signals used to display image information content on the
display device. A specific embodiment of such a computer monitor is
a digital lightbox. The monitor may also be the monitor of a
portable, for example handheld, device such as a smart phone or
personal digital assistant or digital media player.
[0049] The expression "acquiring data" for example encompasses
(within the framework of a data processing method) the scenario in
which the data are determined by the data processing method or
program. Determining data for example encompasses measuring
physical quantities and transforming the measured values into data,
for example digital data, and/or computing the data by means of a
computer and for example within the framework of the method in
accordance with the invention. The meaning of "acquiring data" also
for example encompasses the scenario in which the data are received
or retrieved by the data processing method or program, for example
from another program, a previous method step or a data storage
medium, for example for further processing by the data processing
method or program. The expression "acquiring data" can therefore
also for example mean waiting to receive data and/or receiving the
data. The received data can for example be inputted via an
interface. The expression "acquiring data" can also mean that the
data processing method or program performs steps in order to
(actively) receive or retrieve the data from a data source, for
instance a data storage medium (such as for example a ROM, RAM,
database, hard drive, etc.), or via the interface (for instance,
from another computer or a network). The data can be made "ready
for use" by performing an additional step before the acquiring
step. In accordance with this additional step, the data are
generated in order to be acquired. The data are for example
detected or captured (for example by an analytical device).
Alternatively or additionally, the data are inputted in accordance
with the additional step, for instance via interfaces. The data
generated can for example be inputted (for instance into the
computer). In accordance with the additional step (which precedes
the acquiring step), the data can also be provided by performing
the additional step of storing the data in a data storage medium
(such as for example a ROM, RAM, CD and/or hard drive), such that
they are ready for use within the framework of the method or
program in accordance with the invention. The step of "acquiring
data" can therefore also involve commanding a device to obtain
and/or provide the data to be acquired. For example, the acquiring
step does not involve an invasive step which would represent a
substantial physical interference with the body, requiring
professional medical expertise to be carried out and entailing a
substantial health risk even when carried out with the required
professional care and expertise. For example, the step of acquiring
data, for example determining data, does not involve a surgical
step and for example does not involve a step of treating a human or
animal body using surgery or therapy. In order to distinguish the
different data used by the present method, the data are denoted
(i.e. referred to) as "XY data" and the like and are defined in
terms of the information which they describe, which is then for
example referred to as "XY information" and the like.
[0050] In the field of medicine, imaging methods (also called
imaging modalities and/or medical imaging modalities) are used to
generate image data (for example, two-dimensional or
three-dimensional image data) of anatomical structures (such as
soft tissues, bones, organs, etc.) of the human body. The term
"medical imaging methods" is understood to mean (advantageously
apparatus-based) imaging methods (so-called medical imaging
modalities and/or radiological imaging methods) such as for
instance computed tomography (CT) and cone beam computed tomography
(CBCT, for example volumetric CBCT), x-ray tomography, magnetic
resonance tomography (MRT or MRI), conventional x-ray, sonography
and/or ultrasound examinations, and positron emission tomography.
The image data thus generated is also termed "medical imaging
data". Analytical devices are for example used to generate the
image data in apparatus-based imaging methods. The imaging methods
are for example used for medical diagnostics, to analyse the
anatomical body in order to generate images which are described by
the image data. The imaging methods are also for example used to
detect pathological changes in the human body.
[0051] Image fusion can be elastic image fusion or rigid image
fusion. In the case of rigid image fusion, the relative position
between the pixels of a 2D image and/or voxels of a 3D image is
fixed, while in the case of elastic image fusion, the relative
positions are allowed to change.
[0052] In this application, the term "image morphing" is also used
as an alternative to the term "elastic image fusion", but with the
same meaning.
[0053] Elastic fusion transformations (for example, elastic image
fusion transformations) are for example designed to enable a
seamless transition from one dataset (for example a first dataset
such as for example a first image) to another dataset (for example
a second dataset such as for example a second image). The
transformation is for example designed such that one of the first
and second datasets (images) is deformed, for example in such a way
that corresponding structures (for example, corresponding image
elements) are arranged at the same position as in the other of the
first and second images. The deformed (transformed) image which is
transformed from one of the first and second images is for example
as similar as possible to the other of the first and second images.
For example, (numerical) optimisation algorithms are applied in
order to find the transformation which results in an optimum degree
of similarity. The degree of similarity is for example measured by
way of a measure of similarity (also referred to in the following
as a "similarity measure"). The parameters of the optimisation
algorithm are for example vectors of a deformation field. These
vectors are determined by the optimisation algorithm in such a way
as to result in an optimum degree of similarity. Thus, the optimum
degree of similarity represents a condition, for example a
constraint, for the optimisation algorithm. The bases of the
vectors lie for example at voxel positions of one of the first and
second images which is to be transformed, and the tips of the
vectors lie at the corresponding voxel positions in the transformed
image. A plurality of these vectors are preferably provided, for
instance more than twenty or a hundred or a thousand or ten
thousand, etc. For example, there are (other) constraints on the
transformation (deformation), for example in order to avoid
pathological deformations (for instance, all the voxels being
shifted to the same position by the transformation). These
constraints include for example the constraint that the
transformation is regular, which for example means that a Jacobian
determinant calculated from a matrix of the deformation field (for
example, the vector field) is larger than zero, and also the
constraint that the transformed (deformed) image is not
self-intersecting and for example that the transformed (deformed)
image does not comprise faults and/or ruptures. The constraints
include for example the constraint that if a regular grid is
transformed simultaneously with the image and in a corresponding
manner, the grid is not allowed to interfold at any of its
locations. The optimising problem is for example solved
iteratively, for example by means of an optimisation algorithm
which is for example a first-order optimisation algorithm, for
example a gradient descent algorithm. Other examples of
optimisation algorithms include optimisation algorithms which do
not use derivations, such as the downhill simplex algorithm, or
algorithms which use higher-order derivatives such as Newton-like
algorithms. The optimisation algorithm preferably performs a local
optimisation. If there are a plurality of local optima, global
algorithms such as simulated annealing or generic algorithms can be
used. In the case of linear optimisation problems, the simplex
method can for instance be used.
[0054] In the steps of the optimisation algorithms, the voxels are
for example shifted by a magnitude in a direction such that the
degree of similarity is increased. This magnitude is preferably
less than a predefined limit, for instance less than one tenth or
one hundredth or one thousandth of the diameter of the image, and
for example about equal to or less than the distance between
neighbouring voxels. Large deformations can be implemented, for
instance due to a high number of (iteration) steps.
[0055] The determined elastic fusion transformation can for example
be used to determine a degree of similarity (or similarity measure,
see above) between the first and second datasets (first and second
images). To this end, the deviation between the elastic fusion
transformation and an identity transformation is determined. The
degree of deviation can for instance be calculated by determining
the difference between the determinant of the elastic fusion
transformation and the identity transformation. The higher the
deviation, the lower the similarity, hence the degree of deviation
can be used to determine a measure of similarity.
[0056] A measure of similarity can for example be determined on the
basis of a determined correlation between the first and second
datasets.
[0057] Preferably, atlas data is acquired which describes (for
example defines, more particularly represents and/or is) a general
three-dimensional shape of the anatomical body part. The atlas data
therefore represents an atlas of the anatomical body part. An atlas
typically consists of a plurality of generic models of objects,
wherein the generic models of the objects together form a complex
structure. For example, the atlas constitutes a statistical model
of a patient's body (for example, a part of the body) which has
been generated from anatomic information gathered from a plurality
of human bodies, for example from medical image data containing
images of such human bodies. In principle, the atlas data therefore
represents the result of a statistical analysis of such medical
image data for a plurality of human bodies. This result can be
output as an image--the atlas data therefore contains or is
comparable to medical image data. Such a comparison can be carried
out for example by applying an image fusion algorithm which
conducts an image fusion between the atlas data and the medical
image data. The result of the comparison can be a measure of
similarity between the atlas data and the medical image data.
[0058] The human bodies, the anatomy of which serves as an input
for generating the atlas data, advantageously share a common
feature such as at least one of gender, age, ethnicity, body
measurements (e.g. size and/or mass) and pathologic state. The
anatomic information describes for example the anatomy of the human
bodies and is extracted for example from medical image information
about the human bodies. The atlas of a femur, for example, can
comprise the head, the neck, the body, the greater trochanter, the
lesser trochanter and the lower extremity as objects which together
make up the complete structure. The atlas of a brain, for example,
can comprise the telencephalon, the cerebellum, the diencephalon,
the pons, the mesencephalon and the medulla as the objects which
together make up the complex structure. One application of such an
atlas is in the segmentation of medical images, in which the atlas
is matched to medical image data, and the image data are compared
with the matched atlas in order to assign a point (a pixel or
voxel) of the image data to an object of the matched atlas, thereby
segmenting the image data into objects.
DESCRIPTION OF THE FIGURES
[0059] In the following, the invention is described with reference
to the enclosed figures which represent preferred embodiments of
the invention. The scope of the invention is not however limited to
the specific features disclosed in the figures, wherein
[0060] FIG. 1 is a flow diagram showing i.a. the steps of acquiring
a single patient image and patient meta-information; and
[0061] FIG. 2 illustrates a sample selection slider on the basis of
patient similarity with the sample and number of cases in the
population database in total.
[0062] In a realization of the disclosed method, determination of
patient traits from features originating in patient
meta-information (e.g. age, gender etc., which are acquired as the
patient definition data) is combined with patient traits determined
from the imaging data (i.e. the outcome atlas data) in order to
define a feature vector describing the medical case at hand. The
patient population already loaded into the inference system (i.e.
the acquired general population data) is compared with the patient
at hand (as defined by the patient definition data) and the amount
of similar cases (which constitutes at least a part of the adapted
population data) is selected which is necessary for a statistically
valid inference. Image-based and not image-based aspects of
case-to-sample-similarity are displayed and by using a GUI element
such as a slider (cf. the large arrow in FIG. 2 indicating the
division between the cases from the general population included in
and excluded from, respectively, the adapted population), the
sample size (i.e. the size of the adapted population) can be
increased at the cost of a reduction of case-to-sample-similarity.
Statistical power of the drawn inference is displayed similarly but
will grow with the sample size utilized. The fast calculation of
case-adaptive inferences (i.e. outcome atlases) is supported by the
outlined (see FIG. 1) architecture of utilizing intermediate
inference repositories from which pre-processed results can be
quickly assembled into adapted maps.
[0063] FIG. 1 illustrates that, for determining the patient feature
vector, the individual image defined by medical image data
describing the patient at hand is registered to an atlas to
transfer it into a standard coordinate space and to determine
certain morphological traits of the patient which are then stored
in the patient feature vector. The outcome atlas data stored in the
patient data silo is then searched for members of the general
population which fulfil at least one predetermined criterion for a
similarity with at least one specific characteristic (i.e.
statistical feature) stored in the patient feature vector.
[0064] An outcome atlas such as a resectability atlas fulfils the
purpose of highlighting the probability with which a tumour can be
successfully resected. Calculating the difference between all
available preoperative tumour volumes and all available residual
postoperative tumour volumes which remain after a surgical
intervention on the tumour, volumes can be calculated that
represent the statistically significant average volume of residual
tumour. These volumes are neither directly visible from the
preoperative nor the postoperative images but represent an
intermediary result. These are assembled into the final outcome
atlas (embodying the outcome atlas data) by collecting all of these
volumes in one common frame of reference and expressing the
resectability as a local probability. This last inference assembly
step can be tuned so as to only include patients from the available
patient data population which correspond to the case at hand with
respect to certain similarity features (e.g. age and gender,
presence or absence of genetic properties or morphological
image-properties such as e.g. grey/white-matter tissue ratio).
[0065] Statistical power for a selected subsample of all available
patient data is calculated by analysing the effect size of the
treatment (e.g. surgical resection) in response to the indication
(e.g. low-grade glioma). In the case of resection the effect is
large (tumour disappears), which might not be the case for all
analysed treatments (e.g. radiation treatment or a certain
pharmacological agent lead probably only to a slight shrinkage in
some cases). For treatments with weaker of more distributed effects
more cases need to be assembled for the final outcome map to be of
statistical significance (e.g. if the shrinkage is only minor that
effect has to be put in contrast with "natural" variations or
measurement imprecisions that occur in the data and might obscure
minimal but significant effects).
[0066] With this system the inference logic behind an image-based
recommendation/clinical decision support becomes transparent at the
point where the clinician receives the information--and can decide
to accept or decline this information. The system permits the user
to understand whether the data presently in the clinical decision
support systems really relates to the case at hand or not--which in
turn allows for an understanding of how "regular" the current case
seems to be.
[0067] The image-based and non-image-based patient similarity
selection/display engine which selects the input sample for the
outcome atlas and the intermediate inference layer database from
which the relevant results are selected for result and statistical
validity calculation and final display. The inference assembly
layer utilizes pre-calculated information from the intermediate
inference layer database to assemble the relevant results for the
case into an image (e.g. an outcome atlas). The statistical power
calculator utilizes sample size and effect size on the basis of the
target volume in relation to the sample-covered image volumes.
* * * * *