A system, apparatus and method for mesh registration including an extraction of a preoperative anatomical mesh from a preoperative anatomical image based on a base topology of an anatomical mesh template, an extraction of an intraoperative anatomical mesh from an intraoperative anatomical image based on a preoperative topology of the preoperative anatomical mesh derived from the base topology of an anatomical mesh template, and a registration of the preoperative anatomical image and the intraoperative anatomical image based on a mapping correspondence between the preoperative anatomical mesh and the intraoperative anatomical mesh established by an intraoperative topology of the intraoperative anatomical mesh derived from the preoperative topology of the preoperative anatomical mesh.

A system and method for automatically estimating a hepatorenal index (HRI) from ultrasound images s provided. The method includes acquiring a sequence of ultrasound image data until a desired ultrasound image view is obtained. The method includes segmenting, a liver and a renal cortex in the obtained ultrasound image view. The method includes identifying valid samples in the liver and the renal cortex in the obtained ultrasound image view by excluding invalid samples. The method includes automatically positioning a liver region of interest and a renal cortex region of interest in the obtained ultrasound image view based on the valid samples and at least one criterion. The method includes determining an HRI and causing a display system to present the HRI.

A deep learning model learning device is proposed, including: a parametric MRI image input part inputting an image corresponding to a diagnosis region, inputting at least one parametric MRI image constructed on the basis of parameters different from each other, and constructing and providing an MRI moving image by using the at least one parametric MRI image; a cancer detection model learning part receiving an input of the at least one parametric MRI image and the MRI moving image corresponding to the diagnosis region, and learning a deep learning model on the basis of information labeling the cancer region; a labeling reference information providing part providing at least one reference information contributing to the labeling of the cancer region; and a labeling processing part checking the cancer region input on the basis of the at least one reference information and processing the labeling of the checked cancer region.

A method of providing a quantitative volumetric assessment of organ health or a quantitative map of an organ. The method comprises obtaining a volumetric map of organ health comprising information defining a state of tissue health across at least part of an organ, receiving an input defining at least one organ section, determining an assessment organ volume based at least partly on the at least one defined organ section, calculating an organ-viability measure for the assessment organ volume based at least partly on information within the volumetric map defining the state of tissue health across the organ volume, and outputting an indication of the organ-viability measure.

A plurality of analysis functions each corresponding to an organ are managed, and organ information is stored in such a manner as to correlate with a corresponding type of analysis function. The organ information indicates which of a plurality of regions included in the organ is to be subjected to thinning. Specification of one of the analysis functions is received from a user, and medical image data is acquired. A plurality of regions of an organ included in the acquired medical image data are identified. The identified plurality of regions of the organ, a region to be subjected to thinning is determined on the basis of the stored organ information and the received type of the analysis function. Thinning is performed on the determined region of the organ. An image of the thinned region is displayed together with an image of a region not subjected to thinning.

An image processing apparatus supports detection of pathological change over time in portion of a captured region commonly included in a first image and a second image, the first image and the second image acquired by capturing a subject at different respective times. The image processing apparatus includes an acquisition unit configured to acquire a subtraction image of the first image and the second image representing the pathological change over time as a difference and a recording unit configured to record information indicating said portion using a name different from a name of the commonly included region in a storage unit in association with the subtraction image.

A method for extracting significant texture features of a B-ultrasonic image and application thereof discloses a channel attention mechanism network, i.e. a context activation residual network, which is designed to effectively model the B-ultrasonic liver fibrosis texture information, and which uses the global context information to strengthen important texture features and suppress useless texture features, such that the deep residual network can capture more significant texture information in the B-ultrasonic images. The process can be mainly divided into two phases: training and testing. During the training phase, the context activation residual network may he trained by using the B-ultrasonic image blocks as input and the pathological results of liver biopsy as labels. During the testing phase, the B-ultrasonic image blocks may be input into the trained non-invasive liver fibrosis diagnosis model to obtain the liver fibrosis staging result for each ultrasonic image.

The invention relates to a TMB classification method and system and a TMB analysis device based on a pathological image, comprising: performing TMB classification and marking and pre-processing on a known pathological image to construct a training set; training a convolutional neural network by means of the training set to construct a classification model; pre-processing a target pathological image of a target case to obtain a plurality of target image blocks; classifying the target image blocks by means of the classification model to acquire an image block TMB classification result of the target case; and acquiring an image TMB classification result of the target case by means of classification voting using all the image block TMB classification results. The invention further relates to a TMB analysis device based on a pathological image. The TMB classification method of the invention has advantages of accuracy, a low cost and fast rapid.

An ultrasound diagnostic apparatus includes processing circuitry. The processing circuitry generates a first image based on an echo signal obtained by transmission and reception of ultrasound waves. The processing circuitry acquires a second image that is an image generated by a medical image diagnostic apparatus. The processing circuitry performs a registration of the first image and the second image, by discretely setting a plurality of relative positions of the first image and the second image within a specified range, calculating the similarity between the first image and the second image corresponding to the plurality of relative positions respectively, updating the plurality of the relative positions based on the calculation result of the similarity, and recalculating the similarity corresponding to the plurality of the updated relative positions respectively. The processing circuitry causes a display to display the images obtained after the registration.

The present invention relates to image analysis of pathology images. In order to improve reliability in image analysis of pathology images, a method is provided for providing support in identifying at least one feature of a tissue sample in a microscopic image. The method comprises the steps of providing a first image of a first microscopy 5 modality representing an area of the tissue sample, providing a second image of a second microscopy modality representing the said area of the tissue sample, generating a first high intensity image by applying a first high intensity filter to the first image or a first low intensity image by applying a first low intensity filter to the first image to obtain first information of the at least one feature, generating a second high intensity image by applying 10 a second high intensity filter to the second image or a second low intensity image by applying a second low intensity filter to the second image to obtain second information of the at least one feature, calculating a correlation of an image pair comprising one of the first high intensity image and the first low intensity image and one of the second high intensity image and the second low intensity image for correlating the first information and the second 15 information of the at least one feature, and outputting the calculated correlation for providing support in identifying the at least one feature of the tissue sample.