Patent Description:
Medical imaging datasets - e.g., acquired using Computed Tomography (CT), Magnetic Resonance Tomography (MRT) or X-ray imaging, to give just a few examples - can be analyzed using computer-implemented algorithms. Specifically, it is possible to detect and localize anatomical structures in the medical imaging datasets. A segmentation can be determined that delimits the anatomical structure. Further, once one or more anatomical structures have been detected in localized, it can be helpful to determine a label set. A label set includes one or more labels that provide a textual description of semantic context information of the anatomical structure. Thereby, analysis of the medical imaging dataset by a radiologist or another user can be facilitated.

Sometimes complex anatomical structures need to be labeled. Such complex anatomical structures can include multiple anatomical substructures. Such anatomical substructures can form parts of the greater anatomical structure. Specifically, it would be possible that the anatomical substructures are arranged in one or more spatial sequences, i.e., next to each other in a type of chain. An example would be arteries or blood vessels, e.g., the coronary artery, that include varies sections.

Furthermore, <NPL>. <NPL> discloses a combination of rule-based methods and deep learning methods for automated anatomical labeling.

A need exists for advanced techniques of determining label sets for medical imaging datasets. Specifically, a need exists for advanced techniques for determining label sets including multiple labels for labeling complex anatomical structures, e.g., the coronary artery.

A computer-implemented method of determining a label set is disclosed. The label set includes multiple labels for anatomical substructures of an anatomical structure. The anatomical structure is depicted by a medical imaging data set. The anatomical structure includes the multiple anatomical substructures arranged in multiple sequences. The multiple sequences of the anatomical substructures are joined at bifurcation points. The method includes obtaining a tree-structured segmentation of the anatomical structure in the medical imaging data set. The tree-structured segmentation includes multiple sequences of sections. The multiple sequences of sections are associated with the sequences of substructures of the anatomical structure. The method also includes obtaining, for each section of the tree-structured segmentation, a respective predetermined list of candidate labels and associated probabilities. Also, the method includes obtaining a ruleset of anatomical interrelationships between the substructures of the anatomical structure. The method further includes determining the label set by performing a selection for each section of the tree-structured segmentation, from the respective predetermined list of candidate labels. The selection pertains to a respective label. The selection is based on the associated probabilities and takes into account the rule set of anatomical interrelationships. The selections for the sections of the tree-structured segmentation are performed in accordance with a recursive walkthrough through the tree-structured segmentation, wherein the recursive walkthrough visits at least some sections of the tree-structured segmentation multiple times to determine multiple alternative solutions for the label set.

A computer program or a computer-program product or a computer-readable storage medium includes program code. The program code can be loaded and executed by at least one processor. Upon loading and executing the program code, the at least one processor performs a method of determining a label set. The label set includes multiple labels for anatomical substructures of an anatomical structure. The anatomical structure is depicted by a medical imaging data set. The anatomical structure includes the multiple anatomical substructures arranged in multiple sequences. The multiple sequences of the anatomical substructures are joined at bifurcation points. The method includes obtaining a tree-structured segmentation of the anatomical structure in the medical imaging data set. The tree-structured segmentation includes multiple sequences of sections. The multiple sequences of sections are associated with the sequences of substructures of the anatomical structure. The method also includes obtaining, for each section of the tree-structured segmentation, a respective predetermined list of candidate labels and associated probabilities. Also, the method includes obtaining a ruleset of anatomical interrelationships between the substructures of the anatomical structure. The method further includes determining the label set by performing a selection for each section of the tree-structured segmentation, from the respective predetermined list of candidate labels. The selection pertains to a respective label. The selection is based on the associated probabilities and takes into account the rule set of anatomical interrelationships. The selections for the sections of the tree-structured segmentation are performed in accordance with a recursive walkthrough through the tree-structured segmentation, wherein the recursive walkthrough visits at least some sections of the tree-structured segmentation multiple times to determine multiple alternative solutions for the label set.

A device comprising at least one processor and a memory is disclosed. The memory includes program code that can be executed by the at least one processor. Upon executing the program code, the at least one processor performs a method of determining a label set. The label set includes multiple labels for anatomical substructures of an anatomical structure. The anatomical structure is depicted by a medical imaging data set. The anatomical structure includes the multiple anatomical substructures arranged in multiple sequences. The multiple sequences of the anatomical substructures are joined at bifurcation points. The method includes obtaining a tree-structured segmentation of the anatomical structure in the medical imaging data set. The tree-structured segmentation includes multiple sequences of sections. The multiple sequences of sections are associated with the sequences of substructures of the anatomical structure. The method also includes obtaining, for each section of the tree-structured segmentation, a respective predetermined list of candidate labels and associated probabilities. Also, the method includes obtaining a ruleset of anatomical interrelationships between the substructures of the anatomical structure. The method further includes determining the label set by performing a selection for each section of the tree-structured segmentation, from the respective predetermined list of candidate labels. The selection pertains to a respective label. The selection is based on the associated probabilities and takes into account the rule set of anatomical interrelationships. The selections for the sections of the tree-structured segmentation are performed in accordance with a recursive walkthrough through the tree-structured segmentation, wherein the recursive walkthrough visits at least some sections of the tree-structured segmentation multiple times to determine multiple alternative solutions for the label set.

Various examples disclosed herein pertain to postprocessing of medical imaging datasets.

For instance, medical imaging datasets could be acquired using CT, MRT, or X-ray imaging, to give just a few examples. Other imaging modalities are conceivable, e.g., Positron Emission Tomography or ultrasound imaging.

A medical imaging dataset can include one or more images, e.g., two-dimensional images. A medical imaging dataset can include volumetric three-dimensional data.

One or more algorithms to postprocessing medical imaging datasets can be used, e.g., to extract information. For instance, one or more anatomical structures may be detected and localized in the medical imaging dataset. Depending on the particular field of view, different types of anatomical structures can be detected and localized. For instance, for angiography, it would be possible that the coronary artery is subject to the postprocessing. For instance, where the brain is imaged, carotid arteries or vertrebal arteries could subject to the postprocessing.

As a general rule, complex anatomical structures can be subject to the postprocessing. Such complex anatomical structures can include multiple substructures. These substructures may be arranged in a certain spatial relationship with each other. Specifically, it would be possible that the substructures are arranged in one or more sequences. This means that a spatial sequence or chain of substructures is formed, i.e., a given substructure can be connected to one or more other substructures. This would be typical for arteries, but other examples would be possible, e.g., muscle structures including tendons and muscles.

According to various examples, the complex anatomical structures can include multiple substructures forming multiple sequences. Two or more of the sequences can be joined at bifurcation points. For instance, an artery may bifurcate into two vessels at such a bifurcation point.

For instance, it is possible to represent such complex anatomical structures that include one or more sequences of substructures in a tree-type data structure.

As a general rule, a tree-type data structure can include a root node from which one or more segments extend to one or more child nodes. Some of these child nodes can implement bifurcations, i.e., some of these child nodes can include multiple respective child nodes.

<FIG> illustrates aspects with respect to an example tree-type data structure <NUM>. The specific data structure <NUM> that is depicted in <FIG> is only one possible example and is used to illustrate concepts associated with tree-type data structures in general.

The tree-type data structure <NUM> includes multiple nodes <NUM>-<NUM>.

Specifically, a root node <NUM> is illustrated. Multiple child nodes <NUM>-<NUM> of the root node are illustrated. Adjacent nodes can be referred to as parent node and child node, respectively. For instance, the node <NUM> would be the parent node with respect to the child node <NUM>.

The nodes <NUM>, <NUM>, as well as the root node <NUM> are bifurcations.

The nodes <NUM>, <NUM>, <NUM>, <NUM> are leaf nodes, i.e., form ends of respective sequences of nodes.

Nodes having a similar distance to the root node <NUM> or a bifurcation can be referred to as sibling nodes. For instance, the nodes <NUM> and <NUM> would be sibling nodes.

As will be appreciated, the nodes <NUM>-<NUM> forms sequences. One example sequence <NUM> is illustrated in <FIG>. This sequence <NUM> extends from the root node <NUM> to the child node <NUM>; the child node <NUM> implements a bifurcation such that two further sequences <NUM>, <NUM> extend towards the leafs.

According to various examples, a segmentation of a medical imaging dataset can be obtained in the form of a tree-type data structure. , the segmentation can include sections, i.e., parts of the segmented area, that are respectively associated with or implement nodes.

According to various examples, postprocessing of medical imaging datasets can include multiple tasks. Some tasks are summarized in TAB. <NUM> below.

Hereinafter, details will be specifically described with respect to the task of determining a label set including multiple labels for anatomical substructures of a complex anatomical structure (cf. <NUM>: task III). Techniques will be described that facilitate determining a label set for a complex anatomical structure that includes multiple substructures arranged in sequences. Techniques will be described that facilitate determining a label set using a tree-structured segmentation as input (cf. <NUM>: example I), along with lists of candidate labels and associated probabilities that can be obtained, e.g., from respective classification of the anatomical substructures (cf. <NUM>: example II) associated with different sections of the tree-structured segmentation.

In other words, it is assumed that, based on other postprocessing tasks of the medical imaging dataset, a segmentation of the anatomical structure is available that includes multiple sections associated with different anatomical substructures. The particular algorithms used to provide such segmentation is not germane for the functioning of the techniques described herein. For instance, reference techniques may be used, e.g., see <NPL> or <NPL> for segmentation of the coronary artery.

Such segmentation, can, in particular, be structured in accordance with a tree-type data structure. , a tree-structured segmentation of the anatomical structure can be obtained, the tree-structure segmentation included with multiple sequences of section that are associated with the sequences of substructures.

For illustration, <FIG> illustrates a CT image of the heart. Using an appropriate postprocessing algorithm, it is possible to obtain a segmentation <NUM> of the coronary artery (cf. Along with the segmentation <NUM>, different sections <NUM> of the segmentation <NUM> are provided with a predetermined list <NUM> of candidate labels <NUM> and associated probabilities <NUM>. Such list <NUM> can be obtain as a result of the classification algorithm, e.g., based on classification activation maps or other representation of probabilities of different classification results.

The sections <NUM> can be organized as nodes of a tree-type data structure (cf.

Thus, along with the segmentation of the anatomical structure, further information is available regarding relative arrangement of the sections of the segmentation and associated probabilities of a classification result for the respective anatomical substructures of that anatomical structure. This classification result can serve as a list of candidate labels and associated probabilities.

Then, it is possible to determine a label set. The label set includes multiple labels for the anatomical substructures. This means that labels can be attached as textual description of semantic context information <NUM> different sections of the segmentation. The label set can be determined based on such additional information pertaining to the predetermined list of candidate labels and the associated probabilities.

Various techniques are based on the finding that - beyond the list of candidate labels and the associated probabilities, e.g., as obtained from a classification algorithm - oftentimes additional rules or constraints are available that limit the choice of a particular label for a given substructure/section of the segmentation.

Such ruleset can be seen as prior knowledge regarding possible variances of the anatomical structure. The ruleset can define anatomical relationships, i.e., what combinations of anatomical substructures are potentially possible. The ruleset is provided based on expert knowledge. The ruleset excludes certain combinations of substructures to be in direct neighborhood, and/or the ruleset specifies allowed combinations of substructures to be in direct neighborhood. Such ruleset can thus be used to determine which labels for a given section of the tree-type segmentation are generally allowed or not allowed, e.g., depending on the choice of one or more labels of neighboring sections of the tree-type segmentation. To give an example: Such ruleset may include the possible labels for a parent node of the tree-type segmentation given that a certain label has been assigned to a child node of that parent node. Similarly, such ruleset could specify the allowed labels for a child node, given that the parent node has been assigned a given label. Such ruleset could specify the allowed labels for a node given that one or more sibling nodes have been assigned with labels.

As a general rule, such ruleset of anatomical relationships can be expressed in various data structures. An example data structure implementing such ruleset <NUM> of anatomical interrelationships is shown in <FIG>.

<FIG> illustrates aspects with respect to a ruleset <NUM> of anatomical interrelationships. The ruleset <NUM> is structured to enable determining of valid child labels given the tree-structured segmentation and a label of a section of the tree-structured segmentation represented by a respective parent node. The ruleset <NUM> can be used to determine, for each "parent label" of a parent node for a tree-structure segmentation, one or more allowed "child labels" for one or more child nodes of the parent node.

As illustrated in <FIG>, given a certain label <NUM> of a parent node (i.e., for a respective section of the tree-type segmentation represented by that parent node), the valid labels <NUM> of the one or more child nodes are provided, e.g., in form of a list.

Also, indications of respective likelihoods <NUM> are provided (these likelihoods <NUM> are generally distinct from the probabilities <NUM>, cf. These likelihoods <NUM> could be associated with respective rights of occurrence of the parent label - child labeled pair is expected based on prior knowledge.

Sometimes, the candidate child labels may depend on the count of child nodes that are associated with a parent node for which the label <NUM> is provided. In other words, different lists of candidate labels may be provided depending on the tree structure of the tree-structured segmentation.

Techniques for determining such ruleset <NUM> are not germane for the functioning of the techniques described herein. Accordingly, various options are conceivable for determining such ruleset <NUM>. In one example, it would be possible to determine the ruleset <NUM> based on an annotation process used for obtaining ground truth labels for training a machine-learning algorithm that provides the classification of the segments of the segmentation (cf. <NUM>: task II). For instance, respective manual annotations performed by an expert for parent node - child node pairs can be recorded and expressed in form of the ruleset <NUM>. Respective occurrences can be counted, to thereby obtain the likelihoods <NUM>. Also, the ruleset is generally explainable and can therefore be inspected and modified by experts.

Various techniques are based on the finding that the list of candidate labels (cf. <FIG>: list <NUM> including the candidate labels <NUM> and the associated probabilities <NUM>) for a given section of the tree-structured segmentation and sometimes differ from the list of allowed labels determined based on the ruleset for the same given section. For instance, some candidate labels may not be allowed by the ruleset while the ruleset may on the other hand allow certain labels that are not candidates.

Various techniques are disclosed that facilitate determining the label set considering such discrepancies between the list of candidate labels and constraints imposed by the ruleset.

According to various examples, it is possible to determine the label set taking into account such additional ruleset, beyond the tree-structured segmentation and the lists of candidate labels and associated probabilities obtained for the various sections of the tree-structured segmentation. , the label assignment can be constrained by the ruleset, thereby taking into account additional prior knowledge. Specifically, it is possible to perform a selection, for each segment of the tree-structured segmentation, from the respective predetermined list of candidate labels, to thereby determine the respective label for that segment. This selection can be based, both, on the associated probabilities, as well as taking into account the ruleset of anatomical interrelationships.

Thereby, it is possible to optimize the label said with respect to, both, the predetermined ruleset, as well as the list of candidate labels and the associated probabilities associated with the tree-structured segmentation. The overall quality of the label set can thereby be improved; erroneous labels may be avoided.

As a general rule, various options are available for performing the selection for each segment of the tree-structured segmentation. Specifically, a selection algorithm can be used that iteratively performs, for each segment of the tree-structured segmentation, a respective selection. , a "walkthrough" through the tree-type segmentation, "visiting" each section of the tree-type segmentation can be implemented.

For instance, such selection algorithm could operate recursively. Thus, a recursive walkthrough the tree-structured segmentation would be possible. Such an implementation of the segmentation algorithm is explained below in connection with <FIG>.

<FIG> is a flowchart of a method according to various examples. <FIG> illustrates aspects with respect to a recursion loop implemented by a selection algorithm.

At box <NUM>, a (previously determined, i.e., at a higher recursion layer) label of a parent node of the tree-structured segmentation is obtained.

At box <NUM>, based on the ruleset, all allowed labels for the currently considered node can be determined. The ruleset can specify multiple allowed labels as being valid for this label given the label of the parent node (cf. For instance, only such valid labels may be considered that are also candidate labels.

Then, at box <NUM>, from this list of allowed labels, all permutations can be generated, resulting in a list of possible assignments of the labels to the child nodes.

To be able to take into account the predetermined probabilities associated with the various child nodes of the tree-structured segmentation, this list of allowed labels can be sorted by descending probability, at box <NUM> (cf. <FIG>: probabilities <NUM>).

Sorting can be performed such that the allowed label for the currently considered node having maximum probability is put at the beginning of the list.

Additional sorting criteria can influence the sorting at box <NUM> such as how often a certain rule was seen in the training set etc., i.e., the likelihoods <NUM>.

A label for a child node can then be picked at box <NUM>. This picking can be done by indexing the sorted list of valid labels, and picking the list entry using a certain offset index. The offset index that can be specific to each section of the tree-structured segmentation.

More generally, a picking rule could be considered at box <NUM>. The picking rule can specify which label is picked to propagate to lower recursions.

For instance, if the offset is "<NUM>", the label having the highest probability can be used (at the top of the list).

Thus, the using different offsets result in different candidate solutions for the respective label set at the observed one or more child nodes.

Given one or more candidate solutions, the algorithm can recurse until all sections have labels or the algorithm fails.

Failure happens if the list of valid labels has zero length, i.e., when it is not possible to find a solution compatible with the ruleset; this would be checked at box <NUM> of the recursion and failure would be passed backed to the higher recursion at box <NUM>.

Multiple possible solutions can be sampled by trying multiple sets of offsets and then choosing the solution with the maximum global label likelihood given the label probabilities for each section. In other words, it would be possible that the recursive walkthrough visits at least some sections of the tree-structured segmentation multiple times to determine multiple alternative solutions for the label set. One of these alternative solutions - associated with different picks from the list of valid labels provided by the ruleset - can be selected to eventually determine the label set based on the sums of the probabilities associated with the labels that are chosen for each one of the alternative solutions.

The picking rules for picking offsets / i.e., picking multiple possible solutions and trying out the result by propagating to further recursions is described next. Offsets might be picked randomly (i.e., the picking rule could include a random contribution) or according to a specific schedule. Given that the underlying probabilities are of good quality, one may assume that offset value "<NUM>" is oftentimes the best solution. Therefore, a useful schedule would for example test all zero offset and all one-hot offsets, i.e. all offsets zero except for one section where one might test "<NUM>","<NUM>" or "<NUM>". This means, more generally, that the picking rule can take into account interrelationships between picks at different sections of the tree-type segmentation. Further samples might be two hot and three hot offsets. In a variant of the picking rule, evidence such as geometric features (bifurcation angle, distance to root/parent or main branch, curvature, etc.) or certain landmarks (e.g., left main bifurcation) can be incorporated in addition to the probabilities.

In some examples, it may not be required to a priori pick multiple offsets, i.e., try, a priori, multiple alternative solutions for a given label; rather, it would be possible to visit sections of the tree-structured segmentation multiple times only upon finding no valid solution for the label set based on the ruleset, i.e., if a failure is reported from another recursion.

Note that the above-described implementation of testing multiple alternative solutions for the label set in accordance with picking different offsets is only one of various possible options. As a general rule, it would be possible that the recursive walkthrough through the tree-structured segmentation visits at least some sections of the tree-structured segmentation multiple times in an ordered fashion, wherein such order is in accordance with the probabilities associated with the allowed labels.

<FIG> illustrates aspects with respect to a processing device <NUM>. The processing device <NUM> includes a processor <NUM> and a memory <NUM>. The processor <NUM> can load program code from the memory <NUM> and execute the program code. The processor <NUM> can communicate with remote entities via an interface <NUM>. For instance, the processor <NUM> could receive, via the interface <NUM>, medical imaging datasets, segmentations, and/or rulesets. The processor <NUM> could output, via the interface <NUM>, a label set. The processor <NUM>, upon loading and executing the program code, could perform techniques as described herein, e.g.: Determining a label set; selecting, for each section of a tree-structured segmentation, a respective label from a plurality of candidate labels and taking into account a predetermined ruleset; executing a respective selection algorithm.

An example processing pipeline of data processing that could be implemented by the processor <NUM> is illustrated in <FIG>.

<FIG> is schematically illustrating aspects with respect to a processing pipeline for postprocessing of a medical imaging dataset <NUM>. The medical imaging dataset <NUM> can be processed by a segmentation algorithm <NUM> to obtain a segmentation <NUM>. The segmentation <NUM> includes multiple sections <NUM> and each section <NUM> is associated with a respective list of candidate labels and associated probabilities. This has been explained in connection with <FIG>.

For instance, the segmentation algorithm <NUM> could be a trained algorithm, e.g., a machine-learning algorithm. A convolutional neural network may be used.

A further algorithm <NUM> is used to determine a label set <NUM>, for the tree-type segmentation <NUM>. The further algorithm <NUM> also takes into account the predetermined ruleset <NUM> which may be loaded from, e.g., a database <NUM>. The ruleset <NUM> defines one or more valid labels for a given "child section" of the tree-type segmentation <NUM>, given a previously determined label for a "parent section".

The algorithm <NUM> can, specifically, implement a recursive walkthrough through the sections of the tree-type segmentation <NUM>. A respective recursion has been discussed in connection with <FIG> above.

The label set <NUM> that is obtained by such techniques could take the form as illustrated in <FIG>.

<FIG> schematically illustrates the label set <NUM> where textual description of various sections of the coronary artery are shown next to the coronary artery. Multiple anatomical substructures <NUM>-<NUM>, i.e., sections of the coronary artery, are shown.

<FIG> is a flowchart of a method according to various examples. The method of <FIG> could be executed by a processing device, e.g., the processing device <NUM> according to <FIG>. The method of <FIG> could be executed by a processor such as the processor <NUM>, upon loading and executing program code from a memory such as the memory <NUM>. The flowchart of <FIG> could implement the processing pipeline of <FIG>.

At box <NUM>, it would be possible to obtain a tree-structured segmentation of an anatomical structure. This can include executing a respective segmentation algorithm (cf. <FIG>: segmentation algorithm <NUM>). In other scenarios, it would be possible that the tree-structure segmentation has been pre-prepared and, at box <NUM>, is loaded from a database. For instance, the tree-structure segmentation could be loaded from a picture archiving system, e.g., together with the underlying medical imaging data set.

The tree-structured segmentation includes multiple sections arranged in sequences, i.e., next to each other forming a chain; the sequences correspond to branches of the underlying tree-type data structure. Each sequence can include at least one section. The sequences are also associated with sequences of substructures of the anatomical structure.

At box <NUM>, along with the tree-structured segmentation, for each section of the tree-structured segmentation (implementing nodes of a tree-type data structure), a respective list of candidate labels can be obtained along with associated probabilities.

At box <NUM>, a ruleset of anatomical interrelationships between substructures of the respective anatomical structure can be obtained.

Thus, the ruleset can define one or more valid labels for a given anatomical substructure, depending on the assumption of another assigned label to another anatomical substructure. Thus, meaningless or unrealistic or artificial combinations of labels of adjacent substructures are avoided.

At box <NUM>, it is then possible to determine the label set, taking into account the trees-structured segmentation of box <NUM>, the list of candidate labels for the sections of the tree-structured segmentation as obtained in box <NUM>, as well as the ruleset as obtained in box <NUM>. For instance, for each section, a respective selection of a label can be made, e.g., using a recursive algorithm such as the one as explained in connection with <FIG>.

Summarizing, a data-driven approach for postprocessing medical imaging data sets has been disclosed.

A ruleset of allowable combinations of labels for adjacent substructures of an anatomical structure can be generated from an annotation process for annotating a training set used for training of a segmentation algorithm that segments that anatomical structure. A ruleset can be manually generated. The rules it can be explainable and can be edited.

A data structure for such a ruleset has been disclosed which enables a recursive construction of multiple solutions for a labeling set taking into account the ruleset.

An efficient walkthrough scheme has been disclosed, for implementing the recursive algorithm to make a selection of a label for each section of a tree-structured segmentation.

Sampling can be efficiently implemented using offsets and schedules.

This enables an efficient way to find high-likelihood and consistent solutions.

Empirical testing for labeling of a coronary artery showed a significant improvement of the accuracy. The main classify security for a coronary artery labeling problem with <NUM> labels went from <NUM>% to <NUM>%.

Although the invention has been shown and described with respect to certain preferred embodiments, equivalents and modifications will occur to others skilled in the art upon the reading and understanding of the specification. The present invention includes all such equivalents and modifications and is limited only by the scope of the appended claims.

Claim 1:
A computer-implemented method of determining a label set (<NUM>) of multiple labels (<NUM>) for anatomical substructures of an anatomical structure depicted by a medical imaging dataset (<NUM>), the anatomical structure comprising the multiple anatomical substructures arranged in multiple sequences, the multiple sequences of the anatomical substructures being joined at bifurcation points,
wherein the method comprises:
- obtaining a tree-structured segmentation (<NUM>) of the anatomical structure in the medical imaging dataset, the tree-structured segmentation comprising multiple sequences of sections (<NUM>) associated with the sequences of substructures of the anatomical structure,
- obtaining, for each section of the tree-structured segmentation, a respective predetermined list (<NUM>) of candidate labels (<NUM>) and associated probabilities (<NUM>),
- obtaining a ruleset (<NUM>) of anatomical interrelationships between the substructures of the anatomical structure based on expert knowledge, wherein the ruleset (<NUM>) excludes certain combinations of substructures to be in direct neighborhood and/or specifies allowed combinations of substructures to be in direct neighborhood, and
- determining the label set by performing a selection, for each section of the tree-structured segmentation, from the respective predetermined list of candidate labels of a respective label based on the associated probabilities and taking into account the ruleset of anatomical interrelationships, characterized in that:
wherein the selections for the sections of the tree-structured segmentation are performed in accordance with a recursive walkthrough through the tree-structured segmentation,
wherein the recursive walkthrough visits at least some sections of the tree-structured segmentation multiple times to determine multiple alternative solutions for the label set.