Patent Description:
In clinical practice, often different imaging modalities are used to diagnose or treat a patient. Namely, different imaging modalities may show different aspects of the anatomy of a patient. As such, image data from different imaging modalities may have to be compared or commonly considered. A non-limiting example is that angiograms show the coronaries very well, but the heart itself is only a shadow. As such, the heart may be segmented in Computed Tomography (CT) or Magnetic Resonance (MR) images, and the segmentation then be overlaid onto X-ray images or angiograms to support guidance.

If such image data from different imaging modalities is available, it may be desirable to obtain a segmentation of an anatomical structure, such as an organ or part thereof, across such different imaging modalities. For example, a paper titled "<NPL>, describes a framework for building a linked statistical shape model (LSSM), which is said to be a statistical shape model (SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. It is further said that the framework is particularly relevant in scenarios where accurate boundary delineations of the SOI on one of the modalities may not be readily available, or difficult to obtain, for training a SSM.

However, image data from a particular imaging modality may not always be available. Nevertheless, it may still be desirable to obtain the shape of an anatomical structure in the particular imaging modality, e.g., to perform measurements, to visualize its shape, etc..

Further prior art can be found in <NPL>.

It would be advantageous to obtain a system and method which provides a segmentation of an anatomical structure in a particular imaging modality even if no image from the particular imaging modality is available to the system and method.

The following aspects of the invention involve a) generating and b) using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

A first aspect of the invention provides a system configured for image segmentation, comprising:.

The system may further be configured for generating statistical data for use in image segmentation, the system comprising:.

A further aspect of the invention provides a workstation or imaging apparatus comprising the system.

A further aspect of the invention provides a computer-implemented method for image segmentation, comprising:.

The computer-implemented method may further be configured for generating statistical data for use in image segmentation, the method comprising:.

A further aspect of the invention provides a computer readable medium comprising transitory or non-transitory data representing instructions arranged to cause a processor system to perform the computer-implemented methods.

The above measures involve generating statistical data indicative of a difference in shape of a particular type of anatomical structure (e.g. a difference in physical shape of the underlying, actual or "real" anatomical structure), such as an organ, part of an organ, tissue, etc., between images acquired by two different imaging modalities. For example, a first set of images may be acquired by Ultrasound, and a second set of images may be acquired by MRI. The shape of the anatomical structure may not only vary across a given set of images, e.g., due to patient variability or, when pertaining to a same patient, changes in an anatomical structure over time, but also systematically between the different imaging modalities. Here, the term 'systematic' refers to the differences not residing in incidental differences in the anatomical structure across images, e.g., due to the abovementioned patient variability, but rather in the shape of the anatomical structure structurally differing between both imaging modalities. For example, such systematic differences may be caused by both imaging modalities employing a different imaging geometry, by parts of the anatomical structure being less visible in one of the imaging modalities, etc. Various other causes of such systematic differences may exist as well.

Having obtained two sets of images of the same type of anatomical structure but acquired by different imaging modalities, the difference in shape between both imaging modalities may be determined as follows. Namely, the anatomical structure may be segmented in each image to obtain a segmentation. For example, a mesh model may be used to segment the anatomical structure. Such a segmentation may provide a geometric description of the shape of the anatomical structure, which in turn allows the shape to be easily analyzed. The differences between the segmentations from both imaging modalities may then be determined using statistical analysis. A non-limiting example is that the mean shape of the anatomical structure in each imaging modality may be determined, e.g., in the form of a mean shape mesh model, with the difference then being determined by comparing both mean shapes. However, various other ways of determining the differences between two sets of segmentation may be used as well and are within reach of the skilled person. Here, known techniques from the field of statistical analysis may be used.

Having determined the difference in the shape of the anatomical structure between a first and a second imaging modality, the difference may be made available in the form of statistical data. This may allow a system which has access to an image from the first imaging modality to predict the shape of the anatomical structure as it would appear in an image from the second imaging modality, even if the latter image is unavailable. Namely, the anatomical structure may be segmented in the image, with the segmentation then being modified on the basis of the statistical data to obtain a segmentation of the anatomical structure which is likely to reflect the shape of the anatomical structure in the second imaging modality. Alternatively, the second segmentation may be directly generated using the statistical data and the first segmentation, e.g., without actually modifying the latter.

The above measures have the effect that a segmentation of an anatomical structure in a particular imaging modality may be obtained even if no image from the particular imaging modality is available. Even though this segmentation may represent an estimate or prediction rather than a delineation of the actual shape of the anatomical structure, this may still be advantageous in various scenarios. For example, even though the shape of an anatomical structure between two different imaging modalities may be roughly similar, it may still be desirable to perform the measurement in a specific one of the imaging modalities, e.g., to allow comparison to a "gold standard" which has been determined for this imaging modality, or in general to improve the comparability of measurements.

It will be appreciated that the above measures may also be used to predict the shape of the anatomical structure in further imaging modalities, e.g., a third and subsequent imaging modality, provided that appropriate statistical data is generated and/or available.

Optionally, the set of instructions, when executed by the processor, cause the processor to compute a measurement from the second segmentation of the anatomical structure. For example, the measurement may be a measurement of a volume, the measurement of a distance, the measurement of an area, the measurement of curvature, the measurement of a circumference, the measurement of a diameter, or a combination of one or several of these. The system may thus compute the measurement from the predicted shape of the anatomical structure in the second imaging modality. As also previously stated, this may improve the comparability of measurements, e.g., when past measurements have been performed or a "gold standard" has been determined using the second imaging modality.

Optionally, the image is a pre-interventional image, and the set of instructions, when executed by the processor, cause the processor to overlay the second segmentation of the anatomical structure over an interventional image which is acquired by the second imaging modality. This represents another advantageous use of the second segmentation. Namely, the shape of the anatomical structure may be determined from a pre-interventional image and then, using the statistical data, translated to the expected shape in interventional images and finally overlaid over such images. This may be advantageous in case the interventional images cannot be segmented themselves, e.g., by the segmentation being too computationally complex to be performed in real-time, or by the image quality of the interventional images being insufficient to allow such segmentation to be performed.

Optionally, the first set of images and the second set of images comprise a set of image pairs, wherein each image pair is constituted by an image acquired by the first imaging modality and an image acquired by the second imaging modality, wherein both images of an image pair belong to a same patient. By using pairs of images which relate to a same anatomical structure, the difference in shape between the imaging modalities may be more accurately estimated since differences may be predominately due to the imaging modalities. This advantageous effect may even be obtained when both images are not acquired at the same time or during a same examination, as the differences in the anatomical structure itself will typically be relatively minor, e.g., less than between patients.

Optionally, the set of instructions, when executed by the processor, cause the processor to generate the statistical data by performing a principle component analysis of the first set of segmentations and the second set of segmentations simultaneously. It has been found that principle component analysis (PCA) is well suitable to determine the differences between different sets of segmentations of the type of anatomical structure.

Optionally, the set of instructions, when executed by the processor, cause the processor to generate the statistical data by:.

Optionally, the set of instructions, when executed by the processor, cause the processor to mutually register the first set of segmentations, to mutually register the second set of segmentations and to mutually register the first mean shape and the second mean shape before performing the principle component analysis. By performing a registration between the segmentations, the statistical analysis may focus on the difference in shape rather than a difference in position of the segmentations. For example, the first set of segmentations may all be registered with the first mean shape, second set of segmentations may all be registered with the second mean shape, and the first mean shape and the second mean shape may be mutually registered. Accordingly, all (mean) segmentations may be mutually registered.

Optionally, the set of instructions, when executed by the processor, cause the processor to generate the first set of segmentations and/or the second set of segmentations using model-based segmentation.

It will be appreciated by those skilled in the art that two or more of the above-mentioned embodiments, implementations, and/or optional aspects of the invention may be combined in any way deemed useful.

Modifications and variations of the workstation, the imaging apparatus, either computer-implemented method, and/or the computer program product, which correspond to the described modifications and variations of either or both systems, can be carried out by a person skilled in the art on the basis of the present description.

A person skilled in the art will appreciate that the systems and methods may be applied to image data acquired by various acquisition modalities such as, but not limited to, standard X-ray Imaging, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), and Nuclear Medicine (NM).

Systems and methods are described for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality. Accordingly, the statistical data may also be termed 'shape difference data', but is in this description referred to as statistical data since it is generated based on a statistical analysis of a set of segmentations.

<FIG> shows a system <NUM> for generating the statistical data. The system <NUM> is shown to comprise an image data interface <NUM> configured to access, via data communication <NUM>, a first set <NUM> and a second set <NUM> of images of a type of anatomical structure, with each of the sets having been acquired by a different imaging modality. In the example of <FIG>, the image data interface <NUM> is shown to be connected to an external image repository <NUM> which comprises the image data of the sets of images <NUM>, <NUM>. For example, the image repository <NUM> may be constituted by, or be part of, a Picture Archiving and Communication System (PACS) of a Hospital Information System (HIS) to which the system <NUM> may be connected or comprised in. Accordingly, the system <NUM> may obtain access to the sets of images <NUM>, <NUM> via the HIS. Alternatively, the sets of images <NUM>, <NUM> may be accessed from an internal data storage of the system <NUM>. In general, the image data interface <NUM> may take various forms, such as a network interface to a local or wide area network, e.g., the Internet, a storage interface to an internal or external data storage, etc..

The system <NUM> is further shown to comprise a processor <NUM> configured to internally communicate with the image data interface <NUM> via data communication <NUM>, and a memory <NUM> accessible by the processor <NUM> via data communication <NUM>.

The processor <NUM> may be configured to, during operation of the system <NUM>, segment individual images of the first set of images <NUM> to obtain a first set of segmentations of the type of anatomical structure, segment individual images of the second set of images <NUM> to obtain a second set of segmentations of the type of anatomical structure, and based on the first set of segmentations and the second set of segmentations, generate statistical data <NUM> which is indicative of a difference in shape of the type of anatomical structure between a) the images acquired by the first imaging modality and b) the images acquired by the second imaging modality. <FIG> shows the statistical data <NUM> being output by the processor <NUM>. For example, the statistical data <NUM> may be stored in the memory <NUM> or in another internal or external storage medium. Additionally or alternatively, the system <NUM> may comprise an output interface for outputting the statistical data <NUM>, e.g., to another system.

<FIG> shows a system <NUM> for segmentation based on the statistical data. The system <NUM> is shown to comprise an image data interface <NUM> configured to access, via data communication <NUM>, an image <NUM> which is acquired by a first imaging modality. The image data interface <NUM> may, but does not need to be, a same type of interface as the image data interface of the system of <FIG>. In the example of <FIG>, the image data interface <NUM> is shown to be connected to an external image repository <NUM> which comprises the image data of the image <NUM>. The image repository <NUM> may, but does not need to be, a same type of repository as the image repository described with reference to the system of <FIG>.

The system <NUM> is further shown to comprise a processor <NUM> configured to internally communicate with the image data interface <NUM> via data communication <NUM>, a memory <NUM> accessible by the processor <NUM> via data communication <NUM>, and a user interface subsystem <NUM> with a display processor <NUM> and a user input interface <NUM> which is configured to internally communicate with the processor <NUM> via data communication <NUM>.

The processor <NUM> may be configured to, during operation of the system <NUM>, segment the image <NUM> to obtain a first segmentation of the anatomical structure of the patient, access statistical data indicative of a difference in shape of the type of anatomical structure between a) images acquired by the first imaging modality and b) images acquired by a second imaging modality, and based on the first segmentation and the statistical data, generate a second segmentation of the anatomical structure which represents an estimate of the shape of the anatomical structure of the patient in an image acquired by the second imaging modality. Although the statistical data itself is not shown in <FIG>, it may be accessed by the system <NUM> from a storage medium, e.g., the memory <NUM> or another storage medium, including but not limited to network-accessible storage media. For that purpose, the system <NUM> may comprise an input interface (not shown) such as a network interface.

The user interface subsystem <NUM> may be configured to, during operation of the system <NUM>, enable a user to interact with the system <NUM> via a graphical user interface. For that purpose, the display processor <NUM> may be configured to generate display data <NUM> for a display <NUM> so as to display the graphical user interface to a user. The graphical user interface may be represented by a set of interface instructions stored as data in a memory accessible to the display processor <NUM>, being for example the memory <NUM> or another memory of the system <NUM>. The user input interface <NUM> may be configured to receive user input data <NUM> from a user device <NUM> operable by the user. The user input device <NUM> may take various forms, including but not limited to a computer mouse, touch screen, keyboard, microphone, etc. <FIG> shows the user input device to be a computer mouse <NUM>. In general, the user input interface <NUM> may be of a type which corresponds to the type of user input device <NUM>, i.e., it may be a thereto corresponding type of user device interface <NUM>.

In general, each of the systems of <FIG> and <FIG> may be embodied as - or in - a device or apparatus, such as a workstation or imaging apparatus. The device or apparatus may comprise one or more (micro)processors which execute appropriate software. The processor of each system may each be embodied by one or more of these (micro)processors, or by a same (micro)processor. Software implementing functionality of each system, may have been downloaded and/or stored in a corresponding memory or memories, e.g., in volatile memory such as RAM or in non-volatile memory such as Flash. Alternatively, the processor of each system may be implemented in the device or apparatus in the form of programmable logic, e.g., as a Field-Programmable Gate Array (FPGA). The image data interface and user input interface may be implemented by respective interfaces of the device or apparatus. In general, each unit of each system may be implemented in the form of a circuit. It is noted that each system may also be implemented in a distributed manner, e.g., involving different devices or apparatuses. For example, the distribution may be in accordance with a client-server model, e.g., using a server and a thin-client workstation.

<FIG> indicate the relevance of predicting the shape of an anatomical structure in a second imaging modality from a segmentation of the anatomical structure which is obtained from an image acquired by a first imaging modality. Namely, in clinical practice, measurements are frequently performed on image data from different modalities. An example for such a measurement is the ejection fraction of the left ventricle of the heart or the aortic valve opening area. Depending on the measured value being above or below a certain threshold, a disease may be diagnosed and treatment may be performed.

However, it has been found that there are systematic differences in the results depending on which imaging modality the measurements are performed. This followed from a comparison of a set of Magnetic Resonance (MR) segmentation results and corresponding Ultrasound (US) segmentation results, which involved comparing a US mesh to a corresponding MR mesh for a set of patients. Before a MR mesh and an US mesh were pairwise compared, the US mesh was registered to the MR mesh by applying a point-based transformation comprising rotation, translation, and, depending on the type of comparison, also a scaling. All registrations were performed with respect to the left ventricle, since the right ventricle and the atria were not fully covered by either of the two imaging modalities.

The scaling factors of a rigid point-based transformation including scaling were compared and are shown in <FIG> and <FIG> for the end diastolic (ED) cardiac phase and the end systolic (ES) cardiac phase, respectively, with each figure showing a histogram <NUM>, <NUM> of the scaling factor <NUM>, <NUM> used in the transformation and the vertical axis indicating the occurrence <NUM>, <NUM> of a particular scaling factor. It can be seen that there are systematic differences between the mesh models obtained from Ultrasound images and those obtained from Magnetic Resonance images, with the latter tending to be larger than the former (which is represented by a positive scaling factor). Namely, the MR meshes tended to be larger than the US meshes for the end diastolic cardiac phase, yielding scaling factors between approximately <NUM>% and <NUM>% and having a mean value of <NUM>%. The scaling factor for the end systolic phase varied between <NUM>% and <NUM>% and had a mean value of <NUM>%.

A second registration was performed to investigate the Euclidean distance between the MR mesh and the US mesh. For the second registration, a rigid point-based registration without scaling was applied (rotation and translation). For each triangle in the MR mesh, the closest triangle in the US mesh was determined by calculating the Euclidean distance between the triangles' centers. The Euclidean distance for each triangle was then averaged over all segmentation results and depicted in <FIG> and <FIG>, where the gray levels indicate the mean Euclidian point-to-surface distance between the Ultrasound mesh models and the Magnetic Resonance mesh models for the end diastolic cardiac phase (<FIG>) and the end systolic cardiac phase (<FIG>). Although the exact magnitudes of mean Euclidian point-to-surface distance, e.g., how many millimeters, is not of particular relevance here, it can be seen that these differences are locally distributed rather than globally uniform, indicating that there exists a difference in shape rather than only a difference in size.

It is possible to compensate for such shape differences, or in particular to predict the shape of an anatomical structure in a second imaging modality from a segmentation of the anatomical structure which is obtained from an image acquired by a first imaging modality, as described in the following. Here, an exemplary embodiment is given which is considered as illustrative and as not limiting the invention, of which modifications may be made without departing from the scope of the invention as set forth in the claims.

It may be assumed that a set of S corresponding segmentation results of two imaging modalities are available that show the anatomical structure of interest in a corresponding state, with the anatomical structure of interest being in the following an organ. The corresponding state may, for example, be a same heart phase in the case of cardiac images, or in general there being no interventions between the acquisitions of the images.

Model-based segmentation may be used to segment the organ and to generate a set of corresponding points on the organ surface. An example of such model-based segmentation is described in "<NPL>. An example of a set of M corresponding points on the organ surface is described in "<NPL>. In a specific example, shape-constrained deformable models may be used, e.g., as described in "<NPL>, which may take advantage of the a-priori knowledge about the shape of the object similar to active shape models, but which may also be flexible similar to active contour models.

In general, the models used in both imaging modalities may be the same, e.g., have a same geometry and level of detail, but may also differ in their geometry and/or level of detail. An example of the latter case is that for MR, a shape-constrained deformable cine model may be used which may have <NUM> vertices and <NUM> triangles, whereas for Ultrasound, a shape-constrained deformable model may be used having <NUM> vertices and <NUM> triangles and therefore having a coarser structure compared to the MR model.

In general, the segmentation may result in the shape of the organ of interest being represented as a point distribution model (PDM). The PDM or mesh of the first image modality may comprise M vertices while the mesh of the second image modality may comprise N vertices. Each vertex may represent a three-dimensional vector describing a position in space and may be referred to as <MAT> , with i being an index indicating the patient data set (i ∈ {<NUM>,. , S}) and v giving the vertex number of the PDM (v ∈ {<NUM>,. , M}) or (v ∈ {<NUM>,. The meshes of the first and second imaging modality may then be defined as: <MAT> <MAT>.

As a first processing step, a patient j ∈ i = <NUM>,. , S with a typical organ shape may be selected and a rigid point-based registration may be performed: <MAT> to register the mesh of the second imaging modality yj to the first imaging modality xj resulting in a registered mesh <MAT>. R is the rotation matrix and T the translation vector. A transformation that involved scaling may not be needed since the size difference may be modeled by the shape model of differences, as further described in the following.

In the next step, the remaining meshes xi (i ≠ j) may be aligned to the selected patient xj and the remaining meshes yi (i ≠ j) may be aligned to the registered reference mesh <MAT>. Such alignment may involve a rigid point-based registration (rotation and translation) resulting in <MAT> and <MAT>.

Next, the mean meshes of both modalities may be computed as followed: <MAT> and an eigenvalue analysis of matrix AAt may be performed with: <MAT>.

The shape of the organ-of-interest may now be approximated according to: <MAT> where k = <NUM>,. , p refer to the p greatest eigenvectors and µk and vk refer to the corresponding normalized eigenvectors of the first and second image modality, respectively. The number p of eigenvalues was determined by p = S - <NUM> and wk are weights.

The shape difference between the two imaging modalities may then calculated as: <MAT>.

The above steps may be performed by the system of <FIG>. Having done so, statistical data may be generated which enables the system of <FIG> and similar systems to generate a segmentation of the anatomical structure as it may appear in an image acquired by a second imaging modality based on the statistical data and a first segmentation which is obtained from an image acquired by the first imaging modality. For example, the statistical data may comprise the Eigenvectors of the matrix AAt that belong to the p greatest eigenvectors and the mean meshes x and y. Various alternatives of generating such statistical data are within reach of the skilled person based on this description.

The system of <FIG> or similar system may then perform the following steps. Given a non-training image acquired by the first imaging modality, the organ may be segmented using, e.g., the previously described model-based segmentation. This may result in a segmented organ of interest for the first image modality having M vertices: <MAT>.

In a first step, the given mesh may be registered to the mean mesh of the first imaging modality x leading to a registered mesh x̃reg. The shape of the organ may be approximated by using a weighted sum of the eigenvectors and the mean mesh: <MAT>.

The weights w̃k which may provide the best approximation of the new mesh may be calculated based on: <MAT> which minimizes the difference between the approximated mesh and the original mesh. Here, the "!" over "=" symbol is used to denote that the equation should be zero. With X = x̃ - x and M = (µ<NUM> µ<NUM>. µp), this may be rewritten as: <MAT>.

The weighting factors w̃ = (w̃<NUM> w̃<NUM>. w̃p)T of this overdetermined system may be determined by applying a QR decomposition, e.g., as described in the handbook "<NPL>.

By reformulating the earlier described shape difference y - x, the shape of the organ as it would have been observed in the second imaging modality may now be approximated by: <MAT>.

It is noted that if the numbers of vertices of the mesh of the first imaging modality and the mesh of the second image modality are not the same (M ≠ N), the numbers may be adapted before this equation can be applied, e.g., by using a mapping that maps the vertices of the left ventricle of the US mesh to their corresponding vertices in the MR mesh.

It will be appreciated that various alternative ways of generating and using the statistical data are conceived and are within reach of the skilled person based on this description. In particular, various other statistical analysis techniques may be instead of principle component analysis (PCA), including but not limited to PCA with Orthomax, sparse PCA, Independent component analysis, Maximum autocorrelation factor (MAF) analysis, Kernel PCA, etc. Moreover, alternatively to linear eigenvalue decomposition, also non-linear decompositions may be used. For example, any suitable non-linear eigenvalue decomposition may be used as introduced in section <NUM> of "<NPL> with respect to the generation of a mean shape.

Exemplary use cases include, but are not limited, to the following.

Volume measurements such as the heart chamber volume or the volume of brain structures may be computed from the volume enclosed by the corresponding mesh structure after adapting the mesh model to an image. Similarly, diameter measurements, etc., may be derived from the mesh structure. The described approach allows to approximately compute the measurement as it would have been observed in the second imaging modality or to provide information about the variation of the measurement between different imaging modalities. This information may be of help in, e.g., follow-up studies to assess disease progression or treatment outcome when different imaging modalities have been used.

For example, clinical guidelines and recommendations for clinical measurements, such as the threshold of the fraction ejection of the left ventricle, are usually defined for a specific imaging modality, but are used in clinical practice independently of the imaging modality as it may be laborious and expensive to use several imaging modalities for diagnoses or clinical treatment planning. This may result in inaccurate or erroneous measurements since the measured quantity may vary, e.g., in size or shape, across different imaging modalities. The described approach allows to approximately compute the measurement as it would have been observed in the second imaging modality.

For interventional guidance, pre-operatively acquired models of one imaging modality are often overlaid onto interventional images of a second imaging modality. The described approach allows to compensate shape differences between both modalities and to generate an intra-operative overlay over the interventional images with improved accuracy.

<FIG> shows a computer-implemented method <NUM> for generating statistical data. It is noted that the method <NUM> may, but does not need to, correspond to an operation of the system <NUM> as described with reference to <FIG> and others.

The method <NUM> comprises, in an operation titled "ACCESSING FIRST SET AND SECOND SET OF IMAGES", accessing <NUM> a first set and a second set of images of a type of anatomical structure, wherein the first set of images is acquired by a first imaging modality and the second set of images is acquired by a second imaging modality. The method <NUM> further comprises, in an operation titled "SEGMENTING FIRST SET OF IMAGES", segmenting <NUM> individual images of the first set of images to obtain a first set of segmentations of the type of anatomical structure. The method <NUM> further comprises, in an operation titled "SEGMENTING SECOND SET OF IMAGES", segmenting <NUM> individual images of the second set of images to obtain a second set of segmentations of the type of anatomical structure. The method <NUM> further comprises, in an operation titled "GENERATING STATISTICAL DATA", based on the first set of segmentations and the second set of segmentations, generating <NUM> statistical data which is indicative of a difference in shape of the type of anatomical structure between a) the images acquired by the first imaging modality and b) the images acquired by the second imaging modality. For example, generating <NUM> may comprise generating statistical data which is indicative of a difference in physical shape of the underlying (e.g. actual or real) anatomical structure imaged by the first and second modalities. As described above, the difference in shape represents differences in the shape of the physical anatomy (e.g. the anatomical structure being imaged) which may be due to, for example, the first image modality imaging different parts of the anatomical structure more or less clearly than the second image modality.

<FIG> shows a computer-implemented method <NUM> for segmentation based on the statistical data. It is noted that the method <NUM> may, but does not need to, correspond to an operation of the system <NUM> as described with reference to <FIG> and others.

The method <NUM> comprises, in an operation titled "ACCESSING IMAGE OF PATIENT", accessing <NUM> an image of an anatomical structure of a patient, wherein the image is acquired by a first imaging modality. The method <NUM> further comprises, in an operation titled "ACCESSING STATISTICAL DATA", accessing <NUM> statistical data indicative of a difference in shape of the type of anatomical structure between a) images acquired by the first imaging modality and b) images acquired by a second imaging modality. Operation <NUM> may comprise accessing statistical data generated using the method <NUM> as described above. The method <NUM> further comprises, in an operation titled "SEGMENTING THE IMAGE", segmenting <NUM> the image to obtain a first segmentation of the anatomical structure of the patient. The method <NUM> further comprises, in an operation titled "GENERATING SECOND SEGMENTATION", based on the first segmentation and the statistical data, generating <NUM> a second segmentation of the anatomical structure which represents an estimate of the shape of the anatomical structure of the patient in an image acquired by the second imaging modality.

It will be appreciated that the operations of <FIG> may be performed in any suitable order, e.g., consecutively, simultaneously, or a combination thereof, subject to, where applicable, a particular order being necessitated, e.g., by input/output relations.

Each method may be implemented on a computer as a computer implemented method, as dedicated hardware, or as a combination of both. As also illustrated in <FIG>, instructions for the computer, e.g., executable code, may be stored on a computer readable medium <NUM>, e.g., in the form of a series <NUM> of machine readable physical marks and/or as a series of elements having different electrical, e.g., magnetic, or optical properties or values. The executable code may be stored in a transitory or non-transitory manner. Examples of computer readable mediums include memory devices, optical storage devices, integrated circuits, servers, online software, etc. <FIG> shows an optical disc <NUM>.

Examples, embodiments or optional features, whether indicated as non-limiting or not, are not to be understood as limiting the invention as claimed.

It will be appreciated that the invention also applies to computer programs, particularly computer programs on or in a carrier, adapted to put the invention into practice. The program may be in the form of a source code, an object code, a code intermediate source and an object code such as in a partially compiled form, or in any other form suitable for use in the implementation of the method according to the invention. For example, a program code implementing the functionality of the method or system according to the invention may be sub-divided into one or more sub-routines. An embodiment relating to a computer program product comprises computer-executable instructions corresponding to each processing stage of at least one of the methods set forth herein. These instructions may be sub-divided into sub-routines and/or stored in one or more files that may be linked statically or dynamically. Another embodiment relating to a computer program product comprises computer-executable instructions corresponding to each means of at least one of the systems and/or products set forth herein. These instructions may be sub-divided into sub-routines and/or stored in one or more files that may be linked statically or dynamically.

Claim 1:
A system (<NUM>) configured for image segmentation, comprising:
- an image data interface (<NUM>) configured to access an image (<NUM>) of an anatomical structure of a patient, wherein the image is acquired by a first imaging modality;
- a memory (<NUM>) comprising instruction data representing a set of instructions;
- a processor (<NUM>) configured to communicate with the image data interface and the memory and to execute the set of instructions, wherein the set of instructions, when executed by the processor, cause the processor to:
- segment the image to obtain a first segmentation of the anatomical structure of the patient;
- access statistical data (<NUM>) indicative of a structural difference in shape of the type of anatomical structure between a) images acquired by the first imaging modality and b) images acquired by a second imaging modality; and
- based on the first segmentation and the statistical data, generate a second segmentation of the anatomical structure which represents an estimate of the structural shape of the anatomical structure of the patient in an image acquired by the second imaging modality.