This invention concerns an automatic segmentation method of features, such as anatomical and pathological structures or instruments, which are visible in a 3D medical image of a subject, composed of voxels. Said method being characterised in that it consists in providing a global software means or arrangement combining N different convolutional neural networks or CNNs, with N≥2, and having a structured geometry or architecture adapted and comparable to that of the image volume, and in analysing voxels forming said volume of the 3D image according to N different reconstruction axes or planes, each CNN being allocated to the analysis of the voxels belonging to one axis or plane.

An image processing device includes a processor and a memory that is provided in or connected to the processor. The processor executes a region selection process of selecting a portion of a plurality of tomographic images, which indicate a plurality of tomographic planes of an object, respectively, and have a first resolution, as a target region to be set to a second resolution higher than the first resolution, a resolution enhancement process of increasing the resolution of the target region to the second resolution to generate a high-resolution partial image, and a composite two-dimensional image generation process of generating a high-resolution composite two-dimensional image having the second resolution, using the high-resolution partial image.

The present disclosure relates to methods and systems for vascular image processing. The method may include obtaining an initial vascular image, generating a vascular fragment image by performing a vascular fragmentation operation on the initial vascular image, and generating, based on the vascular fragment image, a vascular centerline image.

An analysis result acquisition unit acquires an analysis result indicating a certainty factor indicating that an abnormality is included in a medical image by analyzing the medical image. A priority deriving unit derives a higher priority as the certainty factor becomes closer to a median value between a maximum value and a minimum value of the certainty factor.

A System for image processing (IPS), in particular for lung imaging. The system (IPS) comprises an interface (IN) for receiving at least a part of a 3D image volume (VL) acquired by PAT an imaging apparatus (IA1) of a lung (LG) of a subject (PAT) by exposing the subject (PAT) to a first interrogating signal. A layer definer (LD) of the system (IPS) is configured to define, in the 3D image volume, a layer object (LO) that includes a representation of a surface (S) of the lung (LG). A renderer (REN) of the system (IPS) is configured to render at least a part of the layer object (LO) in 3D at a rendering view (V.sub.p) for visualization on a display device (DD).

A method includes, in a processor, receiving example representations of geometrical shapes of a given type of organ. In a training phase, a neural network model is trained using the example representations. In a modeling phase, the trained neural network model is applied to a set of location measurements acquired in an organ of the given type, to produce a three-dimensional model of the organ.

A method includes generating first contrast significance data for a first computer vision model generated from a first training set of medical scans. First significant contrast parameters are identified based on the first contrast significance data. A first re-contrasted training set is generated based on performing a first intensity transformation function on the first training set of medical scans, where the first intensity transformation function utilizes the first significant contrast parameters. A first re-trained model is generated from the first re-contrasted training set, which is associated with corresponding output labels based on abnormality data for the first training set of medical scans. Re-contrasted image data of a new medical scan is generated based on performing the first intensity transformation function. Inference data indicating at least one abnormality detected in the new medical scan is generated based on utilizing the first re-trained model on the re-contrasted image data.

A method for gating in tomographic imaging system includes steps of: (a) performing a tomographic imaging on an object with a target moving periodically along a first axis for acquiring projection images; (b) obtaining projected curves by summing up pixel values along a direction of a second axis perpendicular to the first axis in each projection image; (c) determining a target zone on the projection images, wherein a central position on the first axis of the target zone is corresponding to a position having the largest variation in the projected curves on the first axis; (d) calculating parameter values of pixel values in the target zones and obtaining a curve of a moving cycle of the target according to the parameter values; and (e) selecting the projection images under the same state in the moving cycle for image reconstruction according to the curve of the moving cycle of the target.

A surgical guidance system has two cameras to provide stereo image stream of a surgical field; and a stereo viewer. The system has a 3D surface extraction module that generates a first 3D model of the surgical field from the stereo image streams; a registration module for co-registering annotating data with the first 3D model; and a stereo image enhancer for graphically overlaying at least part of the annotating data onto the stereo image stream to form an enhanced stereo image stream for display, where the enhanced stereo stream enhances a surgeon's perception of the surgical field. The registration module has an alignment refiner to adjust registration of the annotating data with the 3D model based upon matching of features within the 3D model and features within the annotating data; and in an embodiment, a deformation modeler to deform the annotating data based upon a determined tissue deformation.

Computer-implemented methods and systems for identifying corresponding slices in medical image data sets are provided. For example, the systems and methods are based on identifying corresponding slices by systematically quantifying image similarities between the slices comprised in one medical image data set and the slices comprised in another medical image data set.