Patent Description:
Various technologies can be used to acquire internal images of human anatomy. Typically, internal images are acquired with a Computed Tomography (CT) scan. However, other imaging modalities, such as Magnetic Resonance (MR), may be used. Lower accuracy applications may rely on ultrasound scans. The raw images acquired using CT and MR scanners can be enhanced and analysed to provide very valuable information, such as accurate measurements of structures in the images.

Measuring and analysing structures in CT or MR images may provide information that serves as one input to a variety of subsequent tasks. One such task may be part of research. In some research projects, there is a need for accurate measurement or delineation of structures in CT or MR images to assess individual organs. Alternatively, research may focus on analyses of differences between organs across a population of different individuals.

In a further alternative, the subsequent tasks may include work by a radiation oncologist. A radiation oncologist may take images, and use them as an input to for example, preparing for treatment of a patient. The delineation of tumours and target volumes in CT or MR images enables a radiation oncologist to better discriminate between organs-at-risk (OARs) and other areas, such as healthy tissue. The radiation oncologist can then develop a 'planning' image, with the aim of sparing healthy tissues in subsequent actions.

When a radiation oncologist reaches the point of planning interventions in radiotherapy, (s)he may use planning software to calculate a treatment plan. That plan typically maximises radiation dose to the target volume and tumour, while minimising dose to the surrounding healthy tissues. Accurate delineation and targeting is very important during treatment planning. This example serves to illustrate the value of any input that can be provided by the enhancement, analysis and interpretation of original CT and MR images.

The process of delineating structures within an image is also known as 'contouring'. The term contouring may be used to indicate the process of delineation in either 2D or 3D, to define the boundary of an anatomical structure. Similarly, the term 'auto-contouring' may be used to indicate the process of producing a contour automatically, and this is discussed further in a separate section below.

Medical scan images of anatomical structures are typically approved by trained radiation oncologists, dosimetrists or radiographers for subsequent treatment planning. Approved anatomical structures may be referred to as 'gold-standard contours'. Gold-standard contours are therefore contours that have been curated and approved by expert clinicians as having high quality. Gold-standard contours are sometimes also termed 'ground-truth' contours. An 'atlas' comprises the pairing of an image and the gold-standard contours for that image. Gold-standard contours are often created in accordance with a given set of contouring guidelines. One such set of contouring guidelines is the 'RTOG' guidelines at: httos://www. org/CoreLab/ContouringAtlases.

The atlases, each comprising gold-standard contours and a corresponding image, can be stored in a database, thereby providing a database of atlases. The atlases are then available for various uses with images that are obtained subsequently. Atlases in the database act as an accumulated store of information, which may greatly improve the processing of subsequent, newly acquired images. Atlases are used to facilitate the contouring of newly acquired patient images, which typically are not images of the same patient as the image from which the atlas and its gold standard contours were derived.

Manual contouring of a medical scan image by a human operator is time consuming, and subject to variability in delineating the anatomical structures. Such variability is due to intra- and inter-operator variation, and due to protocol variations between different medical institutions, or different departments in one medical institution. In fact, different institutions, or even different groups within an institution, may have different contouring guidelines for OARs. Differences between contouring guidelines can exist because the different institutions may subsequently use different protocols for research evaluation or at the stage of planning subsequent treatment. For example, a protocol used at institution A may not be usable at institution B. Within the same institution, different protocols may also be employed, depending for example on the stage and location of a structure such as a tumour.

As a consequence, there may be variations in the gold-standard contours that are generated for a single medical scan image. These variations depend on factors such as the institution/department guidelines and protocols, the state of an organ in the image, and individual operators who process the images.

Given the required amount of time and the inherent variability in defining a gold-standard, contouring represents one of the institutional bottlenecks in the processing of CT and MR images. Delays in image processing may lead to delays in publishing research, or in other subsequent work. Very accurate contours may need to be provided, and such accuracy may take time with known systems.

Auto-contouring methods have been developed. Auto-contouring methods aim, at least in part, to overcome the issues described above.

Auto-contouring methods may generate contours using different technologies. One example of such technologies involves the use of previously delineated cases. Another example of such technologies involves developing model-based approaches. The results of auto-contouring may still require manual editing to account for inaccuracies, and to make the results adhere to an institution or department's contouring guidelines, i.e. to provide a suitable input to subsequent protocols that are in use at that institution or department. In this case, the editing time for the results of auto-contouring can be significantly less than the time that would be required to manually delineate the entire image volume.

The term 'auto-contouring' is a collective description for various underlying approaches. The underlying technological approaches may actually differ widely between systems. These variations in underlying technological approaches are as reported in the literature, see for example reference [<NUM>] in the list of references at the end of this 'Background' section.

In generating any auto-contouring method, the organs within one or more example medical images, normally CT images, are carefully delineated by a clinical expert to generate gold-standard contours following specific contouring guidelines. This combination of a medical image and gold-standard structures is the 'atlas'.

<FIG> illustrates a generic auto-contouring method. Method <NUM> in <FIG> is a generic implementation of a known auto-contouring method. <FIG> is partly a high-level flowchart and partly a schematic diagram.

At step <NUM>, a new image is acquired, for example by a CT or MR scan. The aim of method <NUM> is to contour the new image. At step <NUM>, one of several auto-contouring methods may be applied. Training atlas database <NUM> provides at least one training atlas as an input to step <NUM>.

Each of subsequent <FIG> provides more detailed steps for one way of carrying out the auto-contouring method of step <NUM>. Thus, in <FIG>, the step <NUM> should be seen as representing any of the methods of <FIG>. Regardless of the algorithm used in step <NUM>, step <NUM> will provide estimated contours <NUM>. The estimated contours <NUM> will reflect the contouring style described by the training atlas database <NUM>.

In the case of commercial systems, users typically cannot modify the auto-contouring algorithm that is used in step <NUM>. However, the users might be able to access/modify the training atlas database <NUM>. Examples of such modification might be adding new atlases, in atlas-based contouring.

Traditional approaches to auto-contouring simply rely on the image intensity values and image intensity gradient values. More recent approaches incorporate prior-knowledge about the appearance and shape of the structure that is to be contoured.

Three main methodological approaches amongst these prior-knowledge-based methods are described below. These are 'atlas-based' contouring, statistical models of shape and appearance, and machine learning. An example of atlas-based contouring is shown in <FIG>. An example of shape-model based autocontouring is shown in <FIG>. An example of machine learning-based auto-contouring is shown in <FIG>. The methods of <FIG> may be used alone, or alternatively may also be used in combination.

Atlases are used to generate any of the auto-contouring methods described in <FIG> below. However, atlases are very widely applicable in image interpretation, and are used for other actions than just autocontouring. Thus, in the remainder of this description and the figures, the use of an atlas in any step should not be taken necessarily to imply that atlas-based autocontouring is occurring.

In the remainder of the present application, an atlas database is considered as storing a pool of atlases that are available for selection. Each atlas in the pool of atlases comprises a medical image dataset and at least one defined structure delineating at least one object (e.g. region or organ) within the medical image. Typically, the pool of candidate atlases comprises a large number of candidate atlases, for example potentially hundreds or even thousands of candidate atlases.

When a new patient image is to be contoured, the atlas image/s is/are aligned to the new patient image using deformable image registration. The structures shown in the atlas are then mapped from the atlas to the new patient image, using the estimated deformation field.

Where multiple atlases are available, multiple estimates for the boundary of each organ can be obtained for the patient image. These would be merged into a consensus structure. If contours from multiple atlases have been warped to one new CT image, then each one represents a different estimate of the required contour. In this case, it is possible to obtain the consensus structure, i.e. a single consensus estimate for the required contour. This can be achieved by averaging the warped contours. Alternative approaches are to take a 'majority vote' of the labels that the contours provide at each location, or to use some more advanced statistical method. Such a method is described in <NPL>.

Atlas-based contouring methods are typically limited by the amount of deformation that the registration is able to represent correctly, and by the number and quality of the atlases. Atlas contouring is widely reported in the literature, for example references [<NUM>] and [<NUM>]. One way to improve atlas contouring is to try to select only those atlases which are representative to the new patient image, one example for this method can found in <CIT>.

<FIG> shows an example implementation of a generic atlas-based auto-contouring method <NUM>. Method <NUM> is one example of an implementation of method step <NUM> of <FIG>. The aim of method <NUM> is to contour a new image. At step <NUM>, a new image is acquired, for example by a CT or MR scan, for contouring. At step <NUM>, one or more atlases is selected from training atlas database <NUM>.

At step <NUM>, one or more atlases is brought into registration with the CT image to be contoured. The registration requires a transformation, in order to register the atlas to the CT image that is to be contoured. That transformation is derived, either iteratively or directly, as part of step <NUM>. The transformation is saved and used in step <NUM>.

At step <NUM>, the gold-standard contours are warped from the atlas coordinate system to the coordinate system of the CT image. This warping is done using the transformations estimated at step <NUM>.

At step <NUM>, if more than one atlas was used from step <NUM>, then a consensus contour is generated from the warped contours.

At step <NUM>, the contours can be returned to the user. Thus the CT image that served as the starting point of method <NUM> at step <NUM> has been auto-contoured, and is ready for subsequent use. This use may be, for example, to evaluate a medical image for research purposes, or as an input to subsequent preparations for radiotherapy.

At step <NUM>, one or more atlases were selected. However, the selection of the one or more atlases can be undertaken during a training phase, prior to the start of the auto-contouring method <NUM> on the particular CT image that was acquired at <NUM>.

Statistical models of shape and appearance aim to capture the statistical variations in shape and appearance of an anatomical structure(s) within a given training dataset of atlases. At training time, gold-standard structures and the corresponding CT images need to be available to develop a statistical model, just as for atlas-based contouring. Therefore, it is necessary to build such a training dataset of atlases following specific contouring guidelines. As these methods model the variation in shape and appearance of the training data, their performance is highly dependent on the size and quality of the training atlas database. For instance, the literature in references [<NUM>]-[<NUM>] covers some types of statistical shape and appearance models.

<FIG> illustrates a generic statistical shape and appearance auto-contouring method. <FIG> shows an implementation of a shape and appearance model-based auto-contouring method <NUM>. Method <NUM> is another example of an implementation of method step <NUM> of <FIG>.

Atlas database <NUM> provides a source of atlases. The remainder of method <NUM> is divided into first section <NUM>, which includes the training steps, and second section <NUM>, which includes the testing steps.

Within first section <NUM>, in the training steps, the anatomical variability in shape and size of training ground-truth contours is described using statistical techniques. One example of such a statistical technique is 'Principal Component Analysis'. The image appearance of the training CT images, from atlas database <NUM>, can also be used when generating the statistical model. At the end of training, a statistical shape model will be generated and used for contouring unseen CT images. Within first section <NUM>:.

Within second section <NUM>, during testing, the statistical shape model will be fitted to an unseen, new CT image. At the end of the testing, the resulting contours are returned to theuser. Within second section <NUM>:.

Machine learning refers to a set of mathematical and statistical techniques for learning underlying patterns in training data. 'Deep learning' is one example of machine learning.

Unlike statistical shape and appearance models, machine learning does not explicitly enforce constraints on shapes, or assume that they have particular statistical distributions. Instead, machine learning approaches seek to learn such distributions automatically. For this reason, machine learning methods tend to model variation better than statistical shape models. However, machine learning methods remain limited by the size and content of the training data. Similarly, such approaches need a carefully curated atlas database. The atlas database must have carefully contoured gold-standard cases, which have been contoured to a consistent set of contouring guidelines. Some examples of machine learning methods for auto-contouring in the literature are shown in references [<NUM>] - [<NUM>].

<FIG> illustrates a generic machine learning auto-contouring method <NUM>. Atlas database <NUM> provides a source of atlases. The remainder of method <NUM> is divided into first section <NUM>, which includes training steps, and second section <NUM>, which includes testing steps.

Within first section <NUM>, a mathematical model with trainable parameters is optimised to correlate image features extracted from training CT images and their gold-standard contours. At the end of the training of first section <NUM>, the mathematical model is optimised to predict contours of CT image that was to be contoured. Within first section <NUM>:.

Within second section <NUM>, during testing, the trained model will be run on an unseen, new CT image. At the end of the testing, the resulting contours are returned to user. During testing, the unseen, new CT image is provided as the input to the trained machine learning model, and contours are generated as the output. Within second section <NUM>:.

Comparing <FIG> and <FIG>, it is clear that steps <NUM>, <NUM> and <NUM> of method <NUM> correspond respectively to steps <NUM>, <NUM> and <NUM> respectively of method <NUM>.

The performance of auto-contouring methods is currently hampered by the inherent variation in the definition and execution of the gold-standard contours. The gold-standard contours form the data that is used to build the auto-contouring methods. In practice, in known systems, this has limited the accuracy and robustness of auto-contouring methods.

'Accuracy' typically measures the degree of agreement between the automatic contours and the manually defined gold-standard. 'Robustness' ensures that accurate results are achieved regardless of random and systematic biases in the particular medical image case. Examples and sources of these biases are imaging artefacts, imaging scanner manufacturer, and acquisition protocols.

A robust method may generalize well, in that it provides reasonable average accuracy across a range of institutions. However, a robust method may still fail to produce contours that meet the contouring guidelines for OARs of specific clinical departments or operators. Conversely, an auto-contouring method that is carefully tailored to be accurate for a specific set of contouring guidelines, to provide a set of contours that is suitable for a subsequent treatment planning protocol, is likely to fail drastically when applied to a different set of contouring guidelines. A robust model could be generated using a database populated by multiple institutions. However it is paramount that such a database of atlases is coherently and similarly defined.

All the auto-contouring methods mentioned above can model the variation of the population of images in the atlases available at training time, or, in the case of atlas-based auto-contouring, the variation represented with the selected atlas set. However, given an auto-contouring model generated using a specific training dataset developed to just one contouring standard, an operator will be required to adapt the resulting contours to their institutional/departmental guidelines. Once again, this adaptation is a source of delay, and may introduce further inaccuracies.

To overcome this limitation, a local contouring method to correct for systematic errors introduced by a universal auto-contouring method has been proposed in the literature. See reference [<NUM>] and <CIT>. In Reference [<NUM>], an enhanced approach is provided, which will now be described in detail.

<FIG> illustrates a known local customisation method of auto-contouring a medical scan image. <FIG> shows the use of a local contouring method in accordance with [<NUM>]. Method <NUM> shown in <FIG> uses a machine learning technique. The method <NUM> is referred to as a 'local' method.

The technique of method <NUM> is used to learn to correct for systematic biases introduced by a universal auto-contouring method that uses pre-determined image, contextual and spatial features. In this case, the universal auto-contouring method is not accessible to the operator and cannot be updated or modified. The universal auto-contouring method could be any of the auto-contouring systems described above.

A universal training atlas database <NUM> holds training atlases. A local atlas database <NUM> holds local atlases that are appropriate to a local context, such as a particular institution or medical department.

The method <NUM> starts when there is a CT image <NUM> to contour. However, method <NUM> could start with an MR or other image. At step <NUM>, a universal auto-contouring method operates on the CT image acquired at <NUM>. The universal auto-contouring method applies a contouring style from universal training atlas database <NUM> to CT image <NUM>. The result of step <NUM> is a set of estimated contours <NUM>.

At step <NUM>, the estimated contours <NUM> are then fed to a local contouring method at <NUM>. Local contouring method <NUM> learns from local atlas database <NUM>, to correct the estimated contours <NUM> for systematic biases introduced by step <NUM>. The result is a set of corrected contours <NUM>. The resulting corrected contours <NUM> are then available to the local operator. At step <NUM>, the operator edits and finally approves the corrected contours.

The inventors have realised that, potentially, method <NUM> would allow the incorporation of the approved, corrected contours and the CT image back into the local atlas database <NUM>. However, such incorporation of the approved corrected contours is not shown in reference [<NUM>].

Thus method <NUM> provides edited and approved, corrected contours for the CT image <NUM> that was the starting input to method <NUM>. Those contours are of particular use for just the CT image <NUM> itself. However, in addition, method <NUM> potentially provides a way to expand local atlas database <NUM>. If method <NUM> were repeated for many different initial images such as CT image <NUM>, then over time the local atlas database <NUM> could then provide a reservoir of images. From those images it would be possible to learn how to correct the estimated contours of subsequent images <NUM> for systematic biases introduced by step <NUM>.

Such an approach to expanding the local atlas database <NUM> shown in <FIG> would lead, over time, to the creation of a more useful reservoir of images. Those images could then be used within the institution of their origin.

More generally, all of the types of auto-contouring detailed in <FIG> above would benefit from using a larger dataset in the atlas database. This is true, whether the dataset is to be used to train a model, or to be treated as a set of accepted atlases from which to select a particular subset. However, while method <NUM> would potentially allow expansion of the local atlas database <NUM>, the inventors have realised that there would be significant issues that would prevent the approved local contours and corresponding CT image <NUM> being added to the universal atlas database <NUM>, as explained in the following.

In general, whenever a larger dataset can be made available, most of the methods used for universal auto-contouring would be improved. This is true whether the data are used to train a model, or are treated as a repository from which to select atlases for a multi-atlas contouring step. As described earlier, the inventors have considered that locally curated contouring results could be used to update the local database in method <NUM>, and this would lead to local benefits. However, the inventors have recognised further advantages that could also be achieved if the different centres could all provide their quality assessed results to expand the universal atlas database <NUM>. An expanded universal atlas database <NUM> would improve the universal contouring standard for all centres to then use. There are however, issues that would arise when using approved local contours <NUM>, and the corresponding CT images <NUM>, to expand the universal database <NUM>, as explained in the following.

Returning to method <NUM>, reference [<NUM>] proposed a local auto-contouring method <NUM> to correct for systematic contouring errors produced by a universal auto-contouring method <NUM>. The universal method can be any of the auto-contouring algorithms described in <FIG>. The method <NUM> extends to the local auto-contouring method learning to adapt the contours generated by the universal auto-contouring method to the local guidelines of the institution/department. This part of the process is achieved in step <NUM> and is described in further detail in <FIG>. At training time, method <NUM> is provided with both universal and local contours. It then optimises the parameters of a machine learning algorithm to adjust the output of each set of estimated contours <NUM>, i.e. each universal contour set, so that it matches the corresponding local set as closely as possible. Reference [<NUM>] actually proposed two possible implementations of the algorithm, although the aim of each is the same.

<FIG> illustrates an implementation of a known local customisation method shown in <FIG>. Method <NUM> of <FIG> illustrates a known training implementation, for a local auto-contouring method.

In order to train a local auto-contouring method, the medical image scans, the associated local gold standard contours and the contours generated by the universal auto-contouring system will be considered. Imaging, spatial and contextual features are extracted from the images and from the generated universal contours. Reference [<NUM>] proposed two methods:.

With method <NUM>, local atlas database <NUM> provides local gold standard contours. Universal atlas database <NUM> provides universal auto-contouring contours.

At step <NUM>, a training medical image scan, the local gold standard contours and the universal auto-contouring contours are retrieved. Step <NUM> involves the retrieval of the corresponding contours, i.e. local gold-standard and estimated universal contours, from databases <NUM> and <NUM> associated to the training CT image.

At step <NUM>, method <NUM> finds any voxels that had been mis-labelled by the universal auto-contouring method <NUM>, compared to the local gold-standard contours.

At step <NUM>, method <NUM> extracts imaging and contextual features from the training images and the universal contours. At step <NUM>, an AdaBoost algorithm is trained to correlate imaging and contextual features with correct labels. At step <NUM>, the trained model is returned for subsequent use.

The problems recognised by the inventors in any attempt to expand the universal atlas database, i.e. dataset of training atlases for the universal auto-contouring method, can be understood more clearly from <FIG> and <FIG>. Datasets need to be consistent to be effective, and this can be difficult to achieve with known approaches.

<FIG> illustrates the outcome of applying a known local customisation method of auto-contouring to a medical scan image. <FIG> provides a visual example of the contouring style of the universal and local methods.

For example, in <FIG> a new medical scan image, <NUM>, is to be contoured. The contouring is to use contours <NUM> that have been provided to the contouring standard of images in the universal atlas database <NUM>, i.e. the new medical scan image is to be contoured by a method trained on or using an atlas database <NUM>. The contours shown at <NUM> are an example of the contouring style of the universal auto-contouring method, derived by using the universal training atlas database <NUM>. The universal auto-contouring method can be any of the methods in <FIG>.

The process results in contours that are schematically illustrated as the image <NUM>. Thus the contours shown at <NUM> are contours that are generated by the universal auto-contouring method for medical image scan <NUM>.

By comparing the various contours of the image <NUM> with the original medical scan image <NUM>, it is possible to recognise the effects of the contouring standard using the universal auto-contouring method <NUM>.

The contours shown at <NUM> are then the results of a local method A trained on a local atlas database from institution/department A. The contours reflect the contouring style of institution/department A. The contours shown at <NUM> are the results of another local method B trained on a local atlas database from institution/department B. The contours reflect the contouring style of institution/department B.

Considering again step <NUM> of <FIG>, a local auto-contouring method has been applied to image <NUM>. The local method has corrected the contours for different destination institutions A and B. The result of this correction step is image <NUM> for institution A, and image <NUM> for institution B.

As shown at step <NUM> of <FIG>, institutions A and B can edit images <NUM> and <NUM>, respectively, if required, and accept the contours shown in each of images <NUM> and <NUM>. Each of institutions A and B can choose to contribute its image <NUM>, <NUM> to their corresponding local atlas databases <NUM>. However, it is clear that one image <NUM> has been altered very differently at institution A, providing image <NUM>, than at institution B, providing image <NUM>.

<FIG> illustrates problems that the inventors have recognised would occur with known systems, if an attempt were made with known systems to use the contouring results approved at a local level to update the universal atlas database <NUM>. With known systems, any such attempt would lead to difficulties that can be understood with reference to the specific example illustrated in <FIG>.

In <FIG>, a further medical scan image <NUM> is to be contoured. If a locally approved contour and CT image scan <NUM> were previously added to the universal atlas database and used to update the universal auto-contouring system, the universal contouring standard would be affected as shown in <NUM>. The contouring standard <NUM> is biased towards image <NUM> in <FIG> that is valid for institution A. This can be seen by comparing image <NUM> of <FIG> with image <NUM> in <FIG>.

Medical scan image <NUM> is then contoured using the updated universal standard <NUM>, which results in the contours shown in image <NUM>. However, the correction system that had earlier been tuned to adjust for the systematic universal errors, based on the local atlas <NUM>, is no longer appropriate, because the universal contouring standard <NUM> has changed. Therefore, as illustrated in <FIG>, the system results in images <NUM> and <NUM> for institutions A and B. Each of images <NUM> and <NUM> would not fulfil the local guidelines and would require yet more correction.

<FIG> thus illustrates the outcome of known method, when a universal atlas database is used to contour new data. However, the method <NUM> shows what would happen if the universal atlas database were to be expanded with examples that were customised for and approved by a specific local centre. The processed images that are shown in <FIG> demonstrate that, with known approaches, any attempt to provide additional data by adding more images into the universal atlas database <NUM> would actually degrade the performance of the system. <FIG> thus provides an overview of problem that the present invention seeks to solve.

Reviewing each set of contours in <FIG> in more detail, <NUM> shows a new medical scan image to be contoured. Contours <NUM> are generated from an atlas, by local auto-contouring method A, and are approved by users of institution/department A. If atlases from institution A are used to update the universal auto-contouring system, a bias towards the contouring style of that institution is introduced.

Contours <NUM> are generated by the universal auto-contouring method, using the atlases added from institution/department A. The contouring style is different from the original universal method as shown in <NUM>.

Contours <NUM> are then generated by applying a different local auto-contouring method A on contours <NUM>. The bias introduced by <NUM> propagates to the local contouring system A. These contours might not comply with the guidelines of institution/department A, due to the introduced bias.

Contours <NUM> are generated by applying a different local auto-contouring method B on contours <NUM>. The bias introduced by <NUM> propagates to the local contouring system B. These contours might not comply with the guidelines of institution/department B, due to the introduced bias.

The contours generated by the local contouring methods could be used to update the database of local atlases only. However, with known approaches, the contours generated by the local contouring methods cannot be used to update the universal training database used to generate the universal auto-contouring system, because they introduce biases towards institutional/departmental guidelines.

In accordance with a first aspect of the invention, a method comprising the steps of appended claim <NUM> is provided. In accordance with a second aspect of the invention, an apparatus comprising the features of appended claim <NUM> is provided.

The dependent claims provide further details of embodiments of the invention.

The disclosed invention builds a consistent dataset for automatic contouring systems, for use in a universal atlas database. The dataset for automatic contouring systems is able, after suitable processing to use data generated according to local standards, after suitable processing of the data generated according to local standards in accordance with the invention. These local standards can include institutional variation in image acquisition protocols, treatment planning protocols, contouring guidelines and inter-operator variations.

The approach in the present application enables a standardised auto-contouring solution to be deployed more effectively. This is even the case when multiple institutions are involved, employing different protocols for treatment planning and with different contouring guidelines for OARs.

Thus the disclosed invention addresses the problem of how to build such a consistent atlas dataset within a multi-institution environment. In such an environment, operators are still able to contour to different standards for their own use in the local institution, but they can also contribute to expansion of the universal dataset. Such expansion of the universal dataset in a universal atlas database is to the benefit of all institutions that have access to the universal atlas database. The present invention concerns a method and system for providing a greater degree of standardisation of the data that is available for use in auto-contouring methods.

Further details, aspects and embodiments of the invention will be described, by way of example only, with reference to the drawings. In the drawings, like reference numbers are used to identify like or functionally similar elements.

The present invention will now be described with reference to the drawings specified above. However, it will be appreciated that the present invention is not limited to the specific embodiments herein described and as illustrated in the accompanying drawings. Furthermore, because the illustrated embodiments of the present invention may for the most part, be implemented using electronic components and circuits known to those skilled in the art, details will not be explained in any greater extent than that considered necessary as illustrated below, for the understanding and appreciation of the underlying concepts of the present invention.

In accordance with an embodiment of the invention, a method of generating an atlas for a universal atlas database is provided. The method comprises providing a medical scan image, and performing a universal auto-contouring operation on the medical scan image to generate a set of universal contours for the medical scan image. The method further comprises performing a local auto-contouring customisation operation on the medical scan image, to generate a set of local contours for the medical scan image. Then the method standardises the set of local contours, using a trained model to compensate for biases in the set of local contours, thereby creating a set of standardised global contours for the medical scan image.

The medical scan image and the set of standardised global contours for the medical scan image may then be added to the universal atlas database as an atlas. Furthermore, a local atlas may also be added to the local atlas database, the local atlas comprising the medical scan image and the set of local contours, after the approval or editing of the set of local contours.

<FIG> illustrates a method in accordance with an embodiment of the invention. In the method <NUM> of <FIG>, multiple databases of atlases are available. Universal atlas database <NUM> provides an initial database of atlases of carefully curated data. The data comprises medical image datasets, each dataset comprising a medical scan image with at least one defined structure delineating at least one object, e.g. a region or organ, within the medical image. Each medical image dataset in universal atlas database <NUM> has been carefully curated according to specific contouring guidelines. Universal atlas database <NUM> may comprise thousands of atlases.

There may be one universal atlas database <NUM>. The contents of the universal atlas database <NUM> may be available to many different institutions, which may be spread across a country or the globe. Within each institution, such as a medical research centre or an imaging centre, there may be multiple departments that have access to the universal atlas database <NUM>.

Universal atlas database <NUM> may be generated by means of a standardised contouring method, as for instance defined by international contouring guidelines. We refer to this database as the 'universal' atlas database because of the universal standards with which its contents have been prepared, and also because its contents may have represented contributions that have come from many different local institutions. Universal atlas database <NUM> serves to eliminate any institutional/contouring protocol variation, but does capture patient population and imaging protocol variation.

Also shown in <FIG> is just one local atlas database <NUM>. Local atlas database <NUM> may be one of many local atlas databases, which may each be held at different sites dispersed across the globe. Each local atlas database represents the gold-standard contours available to each institution/department. Thus the atlases in the local atlas database <NUM> describe the variations that are specific to the institutional/departmental guidelines. So a medical research centre or an imaging centre may have one or more local atlas databases <NUM>, in addition to a link that allows access to atlases from the universal atlas database <NUM>. Typically, one or more local atlas databases <NUM> are available per clinical institution.

Method <NUM> starts with the provision of a new medical scan image <NUM> that is to be contoured. A universal auto-contouring operation <NUM> is performed on the medical scan image <NUM>. The universal auto-contouring operation <NUM> generates a set of universal contours <NUM> for the medical scan image <NUM>.

Universal atlas database <NUM> may be used to assist with or to implement universal auto-contouring step <NUM>. The universal auto-contouring operation <NUM> may be performed on the medical scan image <NUM> with input, shown at <NUM>, from the universal atlas database <NUM>. The input from the universal atlas database <NUM> to the universal auto-contouring operation <NUM> may comprise a stored atlas from the universal atlas database <NUM>. In some implementations universal atlas database <NUM> may provide more than one stored atlases for use in universal auto-contouring operation <NUM>. Alternatively, the input from the universal atlas database <NUM> to the universal auto-contouring operation <NUM> may comprise a set of variations between a plurality of atlases stored in the universal atlas database <NUM>.

The universal auto-contouring operation <NUM> may be an atlas-based operation. However, universal auto-contouring operation <NUM> may alternatively or additionally use a shape/appearance model, a machine learning approach, or a deep learning algorithm.

Universal auto-contouring step <NUM> leads to an estimated set of universal contours <NUM>, for example for any OARs for the new medical scan image <NUM>. The set of universal contours <NUM> will reflect the contouring guidelines that were used to create the atlases in the universal atlas database <NUM>.

The next step is to customise the universal contours to the specific institutional/departmental guidelines. To do this, a local auto-contouring customisation operation <NUM> is performed on the medical scan image <NUM>. The local auto-contouring customisation operation <NUM> generates a set of local contours <NUM> for the medical scan image <NUM>. Local atlas database <NUM> may supply one or more local atlases for the local auto-contouring customisation operation <NUM>, i.e. atlases that originate from the institutional/departmental local atlas database <NUM>.

The local auto-contouring customisation operation <NUM> may use machine learning techniques to learn to adapt the universal contours <NUM> to the institutional/departmental guidelines. One example of how to implement such an adaptation would be to use the method of Wang, as illustrated in <FIG>. In this particular implementation, a machine learning classifier learns to detect regions of the image scan which are wrongly classified by the universal auto-contouring method, while a second classifier corrects the outcome of the regions highlighted by the first classifier. Local intensity and appearance features can be used as input to the classifiers.

Local auto-contouring customisation operation <NUM> learns to cope with both systematic and random variations. Such variations are introduced by intra- and inter-operator variability in contouring, and by discrepancies between institutional/departmental guidelines.

The set of local contours <NUM> generated by the local auto-contouring step <NUM> adhere to the local protocol requirements. These local contours <NUM> are, therefore, more accurate with respect to the local gold-standard contours. The institution that operates those local protocol requirements will therefore typically find the set of local contours <NUM> more acceptable than the set of universal contours <NUM>.

The generated set of local contours <NUM> is displayed to the operator for review. As shown as step <NUM>, the displayed local contours <NUM> may require further editing <NUM> before approval. This editing may be able to take account of features or peculiarities of the medical scan image <NUM> that have not been processed optimally in local auto-contouring customisation operation <NUM>, or at some step or steps before this point.

Following any further editing and acceptance <NUM> of the set of local contours <NUM>, the local contours are available for updating the local atlas database <NUM>. At step <NUM>, the set of local contours <NUM>, after approval <NUM>, and the medical scan image <NUM> are added to local atlas database <NUM>. Notably, such a step <NUM> of updating the local database is, for example, not shown in reference [<NUM>].

Method <NUM> of the invention also enables the update of the universal atlas database <NUM>. Method <NUM> aims to add only approved gold-standard local contours to the universal atlas database <NUM>. In order to achieve this, method <NUM> provides compensation for the biases introduced by the local auto-contouring step <NUM>. Adding the set of local contours directly to universal atlas database <NUM> with no prior standardisation would introduce unwanted biases. Those unwanted biases would disrupt subsequent performances of universal auto-contouring operation <NUM>. The biases would also feed through to and disrupt any subsequent local auto-contouring step <NUM> that uses such universal contours, as illustrated in <FIG>.

Standardising step <NUM> of method <NUM> enables a global standardisation of the generated local contours <NUM> after editing/approval <NUM>, which thereby provides a set of standardised global contours <NUM> for the medical scan image <NUM>. Standardising step <NUM>, therefore, ensures compliance with the universal contouring guidelines.

Standardising <NUM> the set of local contours <NUM> to provide the set of standardised global contours <NUM> is done using a trained model. The trained model compensates for biases in the set of local contours <NUM> after editing/approval <NUM>, thereby creating the set of standardised global contours <NUM> that is appropriate for the particular medical scan image <NUM>. The model may have been trained using machine learning, and may have been trained to be able to standardise the accepted local contours <NUM> to ensure conformity to the universal database contouring criteria.

After global standardisation step <NUM>, the set of standardised global contours <NUM> and the corresponding medical image <NUM> are added at step <NUM> to universal atlas database <NUM>. The set of standardised global contours <NUM> is then available as part of a new atlas in universal atlas database <NUM>. The new atlas in universal atlas database <NUM> joins the other atlases that were already stored in universal atlas database <NUM>, and may then be used in a subsequent application of operation <NUM> on another new medical scan image.

In summary, the local contouring customisation operation <NUM> serves to adapt the set of universal contours <NUM> to the local institutional/departmental guidelines. Subsequently, the step of standardising <NUM> the set of approved local contours <NUM> removes the biases due to the local guidelines, thereby allowing method <NUM> to create the set of standardised global contours <NUM> to incorporate as part of the new atlas into the universal atlas database <NUM>. Hence the new atlas is available for subsequent iterations of universal autocontouring operation <NUM>, and so constitutes an expansion of universal atlas database <NUM> and/or an update to the universal auto-contouring model.

When the set of universal contours <NUM> is generated at step <NUM>, the universal contours <NUM> may require editing and cannot be added directly to the universal atlas database <NUM>. When operators assess the quality of the contours before approval at step <NUM>, they only do so according to the local standard. So the set of local contours cannot be added directly to universal atlas database <NUM>. In accordance with the invention, the separate standardising step <NUM> is applied to the approved local contours <NUM>, after editing and acceptance of the local contours <NUM> at step <NUM>.

The ability to perform universal auto-contouring operation <NUM> and local auto-contouring customisation operation <NUM> with the expanded atlas databases <NUM>, <NUM> allows both the models used in steps <NUM> and <NUM> to improve robustness and accuracy. In particular, the expansion of the universal atlas database <NUM> using atlases that have undergone the global standardising <NUM> of the set of local contours ultimately improves the ability of auto-contouring step <NUM> and local auto-contouring step <NUM> to describe both variation of the anatomy within the population and the imaging protocol. A single universal atlas database <NUM> that has been expanded according to method <NUM> can be utilised for step <NUM> in any local institution, i.e. in potentially hundreds of local institutions. Thus the method <NUM> allows auto-contouring to be better adapted to work within a multi-institutional or even a global environment.

Method <NUM> has so far been described for a single medical scan image <NUM>. However, method <NUM> may be repeated for a plurality of medical scan images <NUM>, thereby creating a set of standardised global contours <NUM> for each of those medical scan image <NUM>. A new atlas can then be added to the universal atlas database <NUM> for each of the plurality of medical scan images <NUM>. Each new atlas comprises the medical scan image <NUM> and the set of standardised global contours <NUM> for that medical scan image <NUM>.

Table <NUM> below shows an illustration of the contents of universal atlas database <NUM>. The left column shows ten atlases. The numerical sequence <NUM>-<NUM> is the order in which the atlases were added to the universal atlas database <NUM>. The right column describes the source/origin of each atlas. In a real universal atlas database <NUM>, there might be hundreds of atlases for one particular organ, such as a liver.

As shown in table <NUM>, an initial set of three atlases may be added to the universal atlas database <NUM>, based on the work of a radiographer who has approved contours on medical scan images that have deliberately been taken to start off the universal atlas database <NUM>. These atlases <NUM>-<NUM> may have been available from a pre-existing medical study, for example. The atlases <NUM>-<NUM> are then available for use in step <NUM>, when method <NUM> runs for a new medical scan image taken at one of various institutions, for example as part of a continuation of their research.

The institutions A-D mentioned in rows four to ten of table <NUM> for atlases <NUM>-<NUM> have each derived a set of approved local contours <NUM> for each of one or more images that they have taken for this purpose, or that they hold. After global standardising <NUM> of each set of approved local contours <NUM>, universal atlas database <NUM> can be expanded with the resulting atlases. First institution A provides atlases <NUM>-<NUM>. Second institution B provides atlases <NUM> and <NUM>. Third institution C provides atlas <NUM>. Fourth institution D provides atlas <NUM>.

As described above, method <NUM> may result in the newly created atlas(es) being added to the universal atlas database <NUM>. Alternatively, the first atlas may be added to a second local database that is held by the local institution or department. Such a second local database would be held by the local institution in addition to local atlas database <NUM> shown in <FIG>, and may be available only to that local institution. The second local database would be a database of atlases created in accordance with the operation of method <NUM>, i.e. those produced at step <NUM>. The atlases in the second local database are in contrast to those in the local atlas database <NUM>, which is a repository of atlases that represent the local gold-standard contours available to each institution/department. The second local database may be employed by an institution that wished to benefit from method <NUM>, but when universal atlas database <NUM> is either: (i) Not receiving new atlases at step <NUM>; or (ii) Is receiving new atlases at step <NUM>, but those new atlases are not being made available to the local institution. In effect, a local institution can create a second local database, and populate it with new atlases created in step <NUM>, without being reliant on the operators of universal atlas database <NUM> to use method <NUM>. The operators of local atlas database <NUM> could employ the method <NUM> themselves. Those operators can thereby build the second local database themselves, for their own use.

The local auto-contouring customisation operation <NUM> and subsequent steps may comprise further details, as follows. The local auto-contouring customisation operation <NUM> may comprise adaptation of the universal contours <NUM> to local guidelines of a local institution. This local institution may be the institution that both provides the medical scan image <NUM>, and holds the local atlas database <NUM>. The local institution may perform both the universal auto-contouring operation <NUM> and the local auto-contouring customisation operation <NUM>.

Performing the local auto-contouring customisation operation <NUM> on the medical scan image <NUM> may be performed with input <NUM> from the local atlas database <NUM>. The input from the local atlas database <NUM> to the local auto-contouring customisation operation <NUM> may comprise a local atlas from the local atlas database <NUM>, or may comprise multiple local atlases from the local atlas database <NUM>.

Step <NUM> may comprise providing the set of local contours <NUM> to a user for approval or editing, prior to the step of standardising <NUM> the set of local contours using the trained model. Furthermore, the set of universal contours <NUM> may be used in a training step, which is not shown in <FIG>. The training step comprises comparing the set of universal contours <NUM> to the set of local contours <NUM>, in order to further optimise an algorithm that performs the standardising <NUM> of the set of local contours.

The ability to update both the universal auto-contouring operation <NUM> and the local auto-contouring customisation operation <NUM> with newly generated atlases allows the models to improve robustness and accuracy. This is achieved by increasing their ability to better describe both variation of the anatomy within the population, and the imaging protocol. Furthermore, auto-contouring can be better adapted to work within a multi-institutional environment than was the case with known systems. Method <NUM> could also be either atlas-based or model-based as well, i.e. the new standardised atlases created in method <NUM> and included in the universal atlas database <NUM> can be used to improve the universal contouring operation <NUM> regardless of whether it is implemented with an atlas-based technique (<FIG>) or a model-based technique (<FIG>).

<FIG> illustrates an implementation of a training method for the local contouring customisation operation. The local contouring customisation operation is based on a model, which the method of <FIG> trains, prior to use of the model. In the example of <FIG>, the model is a machine learning model. The local contouring customisation operation is the operation that is applied, after completion of training, in the local customisation operation <NUM> of the embodiment of <FIG>.

The training method <NUM>, shown in <FIG>, for the local customisation operation seeks to predict a local 'gold-standard' contour, starting from the inputs <NUM> of: (i) the training medical image scan; (ii) a set of estimated universal auto-contouring contours <NUM> for the training medical image scan; and (iii) a set of local gold-standard contours <NUM> for the training medical image scan. The set of local gold-standard contours originates from local atlas database <NUM>. The set of estimated universal auto-contouring contours <NUM> originates from performance of the universal auto-contouring operation <NUM> on the training medical image scan.

In order to train the model that is to be used by the local customisation operation <NUM>, the training medical image scan, the set of local gold-standard contours <NUM> and the estimated universal auto-contouring contours <NUM> need to be available for a number of cases.

During training of the machine learning model, image features, derived from intensities, gradients, and local context, are extracted <NUM> from the training medical image scan and from the estimated universal auto-contouring contours <NUM>. These extracted features seek to characterise an underlying pattern in the data, namely the relationship between scan image appearance and contours.

A machine learning algorithm <NUM> is then applied to the extracted features, and is used to estimate local contours from the extracted features. The machine learning algorithm comprises a mathematical model with trainable parameters, which seeks to correlate imaging features to the set of local gold-standard contours <NUM>. This is because the feature extraction is to be optimised as part of the training process. Examples of the machine learning algorithm <NUM> are support vector machines, decision trees or neural networks, which can be used to estimate the local contours from the extracted features. In deep learning algorithms, such as convolutional neural networks, the features are not specified in advance.

During application of the training method <NUM>, the internal parameters of the algorithm are optimised <NUM> based on each performance of the correlation step <NUM>. The optimisation <NUM> is such that the estimated local contours, i.e. the model-predicted local contours, match the set of gold-standard local contours <NUM>, as closely as possible. The extent to which they match is quantified by a cost function, which could be, for instance, the difference in the predicted and gold-standard contours. The cost function may also be described as a 'loss' or 'objective' function. During training, the optimisation <NUM> automatically seeks to derive or shape a model with parameters that minimise the cost function.

When optimisation step <NUM> is complete, the training stage is finished and the trained machine learning model is returned <NUM>. The trained machine learning model is then used <NUM> for predicting the local contours for new medical image scans, for which universal contours have been generated, i.e. in the local contouring customisation operation <NUM> of <FIG>.

<FIG> illustrates details of a training method for the universal standardisation operation that is applied to the set of local contours. The universal standardisation operation is the operation that is applied, after training, in global standardisation step <NUM> of the embodiment of <FIG>. The standardising of the set of local contours in step <NUM> seeks to compensate for local effects introduced in steps <NUM> and <NUM>, and thus to prepare data for use by a wider range, i.e. a global range, of institutions. The result of step <NUM> is the creation of a set of global contours to add to the universal atlas database <NUM>, in step <NUM>, together with the medical scan image.

The training method <NUM>, shown in <FIG>, to derive the standardising operation <NUM> , seeks to predict a universal gold-standard contour. The starting inputs <NUM> are: (i) a training medical image scan; (ii) global gold-standard contours <NUM> for the training medical image scan; and (iii) a set of approved, gold-standard local contours <NUM> for the training medical image scan. The set of approved local contours <NUM> originates from step <NUM> of <FIG>. For training purposes, the training medical image scan, the global gold-standard contours <NUM> and the set of approved local contours <NUM> need to be available for a number of cases.

During training of the machine learning model, features derived from intensities, intensity gradients, and local context are extracted <NUM> from the training medical image scan and from the set of approved local contours <NUM>. These extracted features seek to characterise the underlying pattern in the data, namely the relationship between the image appearance and the contours.

A machine learning algorithm <NUM> is then applied to the extracted features, and is used to estimate universal contours from the extracted features. The machine learning algorithm comprises a mathematical model with trainable parameters, which seeks to correlate imaging features to the set of universal gold-standard contours <NUM>. This is because the feature extraction is to be optimised as part of the training process. During the training, the internal parameters of the algorithm are optimised such that the predicted universal contours match the universal gold-standard contours <NUM> as closely as possible.

Examples of the machine learning algorithm <NUM> are support vector machines, decision trees or neural networks. The machine learning algorithm <NUM> is used to estimate the universal contours from the extracted features. In deep learning algorithms, such as convolutional neural networks, the features are not specified in advance.

During application of the training method <NUM>, the internal parameters of the algorithm are optimised <NUM>. The optimisation <NUM> is such that the estimated universal contours, i.e. the model-predicted universal contours, match the set of universal gold-standard local contours <NUM>, as closely as possible. The extent to which they match is again quantified by a cost function, which could be, for instance, the difference in the predicted and universal gold-standard contours. The cost function may also be described as a 'loss' or 'objective' function. During training, the optimisation <NUM> automatically seeks to derive or shape a model with parameters that minimise the cost function.

When optimisation step <NUM> is complete, the training stage is finished. The trained machine learning model is then returned <NUM>.

The trained machine learning model is used <NUM> in routine operation of method <NUM> for predicting the universal contours that are suitable for inclusion in the universal atlas database <NUM>, at step <NUM>, for new medical image scan images. The prediction will be carried out on a new medical scan image <NUM>, for which a set of approved local contours has already been generated in the local contouring customisation operation <NUM> and approval step <NUM> of <FIG>.

<FIG> illustrates a cloud-based embodiment of the invention. In the embodiment <NUM> of a configuration of the system, universal atlas database <NUM> and universal auto-contouring operation model <NUM> may be stored on a cloud-based server <NUM>.

Cloud-based server <NUM> can be accessed by multiple institutions. Each institution may have exclusive access to the local atlas databases and local customisation and standardisation models. First institution <NUM>, second institution <NUM>, and 'Nth' institution <NUM>, are shown in <FIG>.

Each institution may have exclusive access to a local atlas database, and to local customisation and standardisation models. So a first local atlas database <NUM> is in communication with cloud-based server <NUM>. Similarly, a second local atlas database <NUM>, and the Nth local atlas database N <NUM>, are also in communication with cloud-based server <NUM>.

Each of first <NUM>, second <NUM> and third <NUM> local atlas databases can hold local atlases that are appropriate to their local department or institution. However, using steps <NUM> and <NUM> of <FIG>, each of local atlas databases <NUM>, <NUM> and <NUM> can supply <NUM> standardised contours and the corresponding medical image scans, back to universal atlas database <NUM>. Thus various local departments or institutions can use universal atlases from universal atlas database <NUM>, but can also contribute to expansion of the pool of universal atlases within universal atlas database <NUM>.

Each of first institution <NUM>, second institution <NUM>, and 'Nth' institution <NUM> may therefore be equipped to carry out method <NUM> of <FIG>. First local contouring customisation operation <NUM> and first standardising of a set of local contours <NUM> are carried out by first institution <NUM>. First local contouring customisation operation <NUM> corresponds to step <NUM> of <FIG>, but carried out in first institution <NUM> to the standards required by the local contouring guidelines that are currently in force in first institution <NUM>. If first institution <NUM> implements a step corresponding to step <NUM> in <FIG>, then the first institution <NUM> can expand the list of available local atlases in first local atlas database <NUM>, using local atlases based on the set of local contours and corresponding medical scan image provided by each run of first local contouring customisation operation <NUM> on a medical scan image. The first standardising of a set of local contours <NUM> is the step that provides the set of standardised global contours that will be supplied <NUM> with the corresponding medical scan image, back to universal atlas database <NUM> from first institution <NUM>.

Analogously to first local contouring customisation operation <NUM> and first standardising of set of local contours <NUM>, second institution <NUM> may comprise second local contouring customisation operation <NUM> and second standardising of a set of local contours <NUM>. Second local contouring customisation operation <NUM> will include different steps than those in first local contouring customisation operation <NUM>, because the local contouring guidelines that are currently in force in second institution <NUM> will differ from those in force in first institution <NUM>. In addition, the application of training method <NUM> in second institution <NUM> will have provided a second local contouring customisation operation <NUM> that has at least some differences in its parameters than the parameters of first local contouring customisation operation <NUM>.

Nth institution <NUM> may comprise Nth local contouring customisation operation <NUM> and Nth standardising of a set of local contours <NUM>.

<FIG> illustrates a further cloud-based embodiment of the invention.

<FIG> illustrates a variation of the cloud-based portions of the embodiment <NUM> of the system shown in <FIG>. In the embodiment <NUM>, the cloud-based server <NUM> differs from the cloud-based server <NUM> of the embodiment <NUM>.

A universal auto-contouring model <NUM> and universal auto-contouring operation <NUM> are stored on the cloud-based server <NUM>.

<FIG> illustrates a further cloud-based embodiment of the invention. <FIG> illustrates a variation of the embodiment <NUM> of the system shown in <FIG>.

In the embodiment <NUM>, universal auto-contouring operation model <NUM> may be stored on a cloud-based server <NUM>. A first universal atlas database <NUM> is provided in first institution <NUM>. Otherwise, first institution <NUM> comprises first local contouring customisation operation <NUM> and first standardising of set of local contours <NUM>, which function similarly to the corresponding elements in <FIG>. Analogously, second institution <NUM> comprises second local contouring customisation operation <NUM> and second standardising of set of local contours <NUM>. Nth institution <NUM> comprises Nth local contouring customisation operation <NUM> and Nth standardising of set of local contours <NUM>.

So a first local atlas database <NUM> is in communication with cloud-based server <NUM>. Similarly, second local atlas database <NUM>, and the Nth local atlas database N <NUM> are also in communication with cloud-based server <NUM>.

Each of first institution <NUM>, second institution <NUM>, and 'Nth' institution <NUM> may be equipped to carry out method <NUM> of <FIG>. In contrast to the embodiment <NUM> of <FIG>, each of first institution <NUM>, second institution <NUM>, and 'Nth' institution <NUM> has its own version of the universal auto-contouring operation. First institution <NUM> has first universal auto-contouring operation <NUM>. Second institution <NUM> has second universal auto-contouring operation <NUM>. Nth institution <NUM> has Nth universal auto-contouring operation <NUM>.

Considering for example first institution <NUM>, first universal auto-contouring operation <NUM> may be updated with medical scan images and a corresponding set of global contours that have been generated in a step corresponding to step <NUM> in <FIG>, which has been performed in first institution <NUM>. Over time, the first <NUM>, second <NUM> and third universal auto-contouring operations <NUM> will diverge, as various different updates are made to each.

<FIG> illustrates a workstation <NUM> that implements the invention. <FIG> shows a hybrid scanner <NUM> in accordance with the invention. Hybrid scanner <NUM> comprises keyboard <NUM>, mouse <NUM> and display screen <NUM>, which facilitate communication with a user of the hybrid scanner <NUM>.

Hybrid scanner <NUM> also comprises a control module <NUM> that controls a scanning unit <NUM>, to provide scan datasets. These may be images of tissue, of a subject inside scanning unit <NUM>. Medical image scan datasets are produced using different scanning modes. Multi-volume datasets using the same scanning mode can also be provided. Control module <NUM> is linked to memory <NUM>. Memory <NUM> is configured to store a medical scan image <NUM>.

Hybrid scanner <NUM> also comprises a processor <NUM>. Processor <NUM> is configured to:.

Display screen <NUM> displays some or all of the medical scan images and contour sets. Processor <NUM> may perform steps <NUM> and <NUM> of <FIG>. In this case, display screen <NUM> may, for example, display an image with universal contours <NUM> of <FIG> and an image with local contours <NUM> of <FIG>.

Keyboard <NUM>, mouse <NUM> and screen <NUM> allow a user to make edits and indicate approval <NUM> of the local contours shown at step <NUM> in <FIG>.

Analysis module <NUM> may also implement each of steps <NUM>, <NUM> and <NUM> of <FIG>. Each of steps <NUM>, <NUM> and <NUM> of <FIG> may be implemented under the control of the user via interaction with keyboard <NUM>, mouse <NUM> and screen <NUM>.

<FIG> provides an embodiment of a method of training a local contour standardisation model, i.e. the model that will implement the 'standardising of the set of local contours' in step <NUM> of FIG. <FIG> had already provided a general illustration of a method of training a local contour standardisation model. However, <FIG> provides details of the embodiment of the training method that is claimed in appended independent claims <NUM>.

Method <NUM> comprises an initial step of providing a training medical scan image <NUM> to the model, a set of approved local contours for the training medical scan image <NUM>, and providing a set of globally approved universal contours for the training medical scan image <NUM>.

Method <NUM> then generates <NUM> an estimated set of universal contours, from the set of approved local contours and the training medical scan image <NUM>. An algorithm is used to generate <NUM> the estimated set of universal contours. The algorithm used in step <NUM> may comprise a machine-learning or deep-learning algorithm estimating universal contours from the extracted features, to provide the estimated set of universal contours.

A set of differences is evaluated <NUM> between the estimated set of universal contours and the set of globally approved universal contours. Based on these differences, the internal parameters of the model are optimised <NUM> to provide a match between the estimated set of universal contours generated by the algorithm and the set of approved universal contours.

A decision <NUM> is made whether the model has reached the required degree of optimisation. If the answer is 'NO', then another training medical scan image, another set of approved universal contours, and another set of approved local contours will be provided <NUM>. The method <NUM> then returns to step <NUM>. Steps <NUM> to <NUM> will then be repeated for as many other medical scan images <NUM> and their sets of approved local contours and globally approved universal contours as necessary, until the evaluated <NUM> set of differences is smaller than a given threshold.

When the answer at decision box <NUM> is 'YES', then the model can subsequently be used <NUM> to analyse new medical scan images as explained in connection with method <NUM> of <FIG>. Such use of the model thereby creates new atlases for the universal atlas database <NUM> of <FIG>.

Steps <NUM> to <NUM> above have been described in terms of the training medical scan images and their contours going through steps <NUM>-<NUM> one at a time, with a decision for each training medical scan image on whether the method has reached an optimal state. However, method <NUM> does not have to work in this way. In an alternative sequence, the training medical scan images and their contours can be passed through in batches. When the training medical scan images and their contours are passed through in batches, the extent to which the model has been optimised can still be measured with a loss function. The loss function measures the extent of agreement! disagreement between the estimated universal contours and the approved universal contours. The loss function can be chosen from a variety of possible loss functions. Examples of loss functions can be obtained from measures such as the average Hausdorff distance between contours or the Dice overlap between contours. Loss functions can also be derived from multiple such measures.

Step <NUM> may comprise extracting features from each of the medical scan image <NUM> and the approved local contours, the features being derived from intensities, intensity gradients and/or local context.

Step <NUM> may then comprise optimising internal parameters of the algorithm to provide a match between the estimated set of universal contours generated by the algorithm and the set of the globally approved universal contours. Step <NUM> may comprise calculating a cost function, the cost function evaluating the set of differences between the estimated set of universal contours and the set of globally approved universal contours. The cost function is then used to estimate an extent of the match, and the cost function is then minimised.

The trained model, may be used to estimate sets of universal contours for medical scan images from a research project conducted at multiple institutions. The trained model may then be used to populate a single universal atlas database <NUM> with new atlases, the single universal atlas database <NUM> being accessible remotely by the multiple institutions.

A computer program product in accordance with the invention has executable code for a method in accordance with the invention. The invention may be implemented in a computer program for running on a computer system, at least including code portions for performing steps of a method according to the invention when run on a programmable apparatus, such as a computer system or enabling a programmable apparatus to perform functions of a device or system according to the invention.

The computer program may be stored internally on a tangible and non-transitory computer readable storage medium or transmitted to the computer system via a computer readable transmission medium. The computer system may for instance include at least one processing unit such as a CPU or ASIC, associated memory and a number of input/output (I/O) devices. When executing the computer program, the computer system processes information according to the computer program and produces resultant output information via I/O devices.

Claim 1:
A computer-implemented method (<NUM>) of generating an atlas for a universal multi-institution atlas database (<NUM>), the method (<NUM>) comprising:
a) providing a medical scan image (<NUM>);
b) performing a universal auto-contouring operation (<NUM>) using the universal multi-institution atlas database (<NUM>) on the medical scan image (<NUM>), to generate a set of universal contours (<NUM>) for the medical scan image (<NUM>);
c) performing an institution specific auto-contouring customisation operation (<NUM>) using a local atlas database (<NUM>) on the medical scan image (<NUM>), to generate a set of local contours (<NUM>) for the medical scan image (<NUM>);
d) providing the set of local contours (<NUM>) to a user for approval or editing (<NUM>), adding approved local contours and medical scan image (<NUM>) to the local atlas database (<NUM>);
e) standardising (<NUM>) the set of local contours (<NUM>) to ensure compliance with universal contouring guidelines, using a trained model to compensate for biases in the set of local contours (<NUM>), thereby creating a set of standardised global contours (<NUM>) for the medical scan image (<NUM>); and
f) adding (<NUM>) a universal atlas to the universal multi-institution atlas database (<NUM>), the universal atlas comprising the medical scan image (<NUM>) and the set of standardised global contours (<NUM>) for the medical scan image (<NUM>).