Systems and computer-implemented methods of performing image registration. One method includes receiving a first image and a second image acquired from a patient at different times and, in each of the first image and the second image, detecting an upper boundary of an imaged object in an image coordinate system and detecting a lower boundary of the imaged object in the image coordinate system. The method further includes, based on the upper boundary and the lower boundary of each of the first image and the second image, cropping and padding at least one of the first image and the second image to create an aligned first image and an aligned second image and executing a registration model on the aligned first image and the aligned second image to compute a deformation field between the aligned first image and the aligned second image.

One or more example embodiments of the present invention relates to a computer-implemented method for providing an outline of a lesion in digital breast tomosynthesis includes receiving input data, wherein the input data comprises a reconstructed tomosynthesis volume dataset based on projection recordings, a virtual target marker within a lesion being in the tomosynthesis volume dataset; applying a trained function to at least a part of the tomosynthesis volume dataset to establish an outline enclosing the lesion, the part of the tomosynthesis volume dataset corresponding to a region surrounding the virtual target marker in the tomosynthesis volume dataset; and providing output data, wherein the output data is an outline of a two-dimensional area or a three-dimensional volume surrounding the target marker.

A computer device obtains a medical image set. The device identifies a difference between the reference medical image and the target medical image to obtain a candidate non-lesion region in the target medical image. The device determines area size information of the candidate non-lesion region as candidate area size information. The device adjusts the candidate non-lesion region according to the annotated area size information when the candidate area size information does not match the annotated area size information, so as to obtain a target non-lesion region in the target medical image.

The application relates to devices and methods for analysing a colonoscopy video or a portion thereof, and for assessing the severity of ulcerative colitis in a subject by analysing a colonoscopy video obtained from the subject. Analysing a colonoscopy video comprises using a first deep neural network classifier to classify image data from the subject colonoscopy video or portion thereof into at least a first severity class (more severe endoscopic lesions) and a second severity class (less severe endoscopic lesions), wherein the first deep neural network has been trained at least in part in a weakly supervised manner using training image data from a plurality of training colonoscopy videos, the training image data comprising multiple sets of consecutive frames from the plurality of training colonoscopy videos, wherein frames in a set have the same severity class label. Devices and methods for providing a tool for analysing colonoscopy videos are also described.

In a general aspect, enhancement of artificial intelligence algorithms using 3D data is described. In some aspects, input data of an object is stored in a storage engine of a system. The input data includes first-order primitives and second-order primitives. A plurality of features of the object is determined by operation of an analytics engine of the system, based on the first-order primitives and the second-order primitives. A tensor field is generated by operation of the analytics engine of the system. The tensor field includes an attribute set, which includes one or more attributes selected from the first-order primitives, the second-order primitives, or the plurality of features. The tensor field is processed by operation of the analytics engine of the system according to a series of artificial intelligence algorithms to generate output data representing the object.

This disclosure relates to a model training method and apparatus and an image processing method and apparatus. The model training method includes: obtaining a first sample image and a first standard region proportion corresponding to a first object in the first sample image; obtaining a standard region segmentation result corresponding to the first sample image based on the first standard region proportion; and training a first initial segmentation model based on the first sample image and the standard region segmentation result, to obtain a first target segmentation model.

Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles. A predictor such as a convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.

The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within a measurement zone (108). The magnetic resonance imaging system (100) comprises: a processor (130) for controlling the magnetic resonance imaging system (100) and a memory (136) storing machine executable instructions (150, 152, 154), pulse sequence commands (140) and a dictionary (144). The pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands (140) are further configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary (144) comprises a plurality of tissue parameter sets. Each tissue parameter set is assigned with signal evolution data pre-calculated for multiple SSFP states.

In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.