The invention relates to a method for determining the spatial development of tumor tissue, by acquiring patient medical image data describing sequences of patient medical images of tumors in parts of patient bodies, wherein the patient medical images of each sequence have been taken at subsequent points in time and each sequence has been taken tier a different patient; determining, by additively fusing subsequent patient medical images of each sequence to one another, patient spatial development data describing the spatial development of a tumor in each patient body; acquiring atlas data describing an atlas representation of the parts of patient bodies; determining, based on the atlas data and the patient development data, development probability data describing a probability for a spatial development of a tumor.

Systems and methods are provided for segmenting tissue within a computed tomography (CT) scan of a region of interest into one of a plurality of tissue classes. A plurality of atlases are registered to the CT scan to produce a plurality of registered atlases. A context model representing respective likelihoods that each voxel of the CT scan is a member of each of the plurality of tissue classes is determined from the CT scan and a set of associated training data. A proper subset of the plurality of registered at lases is selected according to the context model and the registered atlases. The selected proper subset of registered atlases are fused to produce a combined segmentation.

The invention relates to a computer-implemented medical data processing method for determining a mapping of medical image content into a reference system, the method comprising executing, on a processor of a computer, steps of: a) acquiring, at the processor, medical image data describing a digital medical image of an anatomical structure of a patient's body; b) acquiring, at the processor, image attribute data describing attribute information associated with the medical image data, the attribute information including an indication of an initial reference system in which spatial relationships of the digital medical image are defined; c) acquiring, at the processor, reference system transformation data describing a spatial relationship (REG) between the initial reference system and a second reference system which is different from the initial reference system; d) determining, by the processor and based on the medical image data and the reference system transformation data, transformed image data describing a representation of the digital medical image in the second reference system.

Some embodiments are directed to a segmentation of medical images. For example, a medical image may be registering to multiple atlas images after which a segmentation function may be applied. Multiple segmentation may be fused into a final overall segmentation. The atlas images may be selected on the basis of high segmentation quality or low registration quality.

A dynamic anatomic atlas is disclosed, comprising static atlas data describing atlas segments and dynamic atlas data comprising information on a dynamic property which information is respectively linked to the atlas segments.

A neuroimaging-based approach to predict treatment response in mental disorders by acquiring and analysing brain PET dopamine measures from patients. The method uses a short, simplified protocol for [18F]FDOPA brain PET imaging adapted for clinical practice. Individual [18F]FDOPA brain PET data are then quantified with a fully-automated analysis pipeline to extract information on the dopamine function of the subject. This information coupled with clinical information is run through a prediction algorithm to identify those patients whose illness will not respond to conventional antipsychotics.

Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.

A method (900) of generating an atlas for a universal atlas database (901) is provided. A new medical scan image (905) is provided. A universal auto-contouring operation (920) is performed on the medical scan image, to generate a set of universal contours (930) for the medical scan image. A local auto-contouring customisation operation (940) is performed on the medical scan image, to generate a set of local contours (950) for the medical scan image. The set of local contours is standardised (980) using a trained model to compensate for biases in the set of local contours, thereby creating a set of standardised global contours (985) for the medical scan image. The set of standardised global contours (985) and the medical scan image (905) can be added to the universal atlas database (901) as a new atlas, thereby expanding the set of atlases that are available in the universal atlas database.

A controller for preparing an image for segmenting includes a memory that stores instructions, and a processor that executes the instructions. When executed by the processor, the instructions cause the controller to perform a process that includes displaying a first modeled tissue structure of a first type, and displaying an image of a first tissue structure of the first type separate from the first modeled tissue structure. The process also includes identifying, on the first modeled tissue structure, landmarks on the first modeled tissue structure for identification on the image of the first tissue structure, and sequentially accentuating each landmark on the first modeled tissue structure. The processor identifies locations on the image of the first tissue structure for each landmark on the first modeled tissue structure. The landmarks on the first modeled tissue structure are mapped to the locations identified on the image of the first tissue structure.

Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.