This application relates to a method of measuring a volume of an organ. In one aspect, the method includes acquiring a plurality of captured images of the organ and photographing metadata and preprocessing the plurality of images to acquire a plurality of image patches of a specified size. The method may also include inputting the plurality of image patches into a three-dimensional (3D) convolutional neural network (CNN)-based neural network model and estimating an organ region corresponding to each of the plurality of image patches. The method may further include measuring a volume of the organ by using an area of the estimated organ region and the photographing metadata. The method may further include measuring an uncertainty value of the 3D CNN-based neural network model and uncertainty values of the plurality of images based on a result of estimating by the 3D CNN-based neural network model.

Systems and methods are provided for computer aided phenotyping of fibrosis-related conditions. A digital image indicates presence of collagens in a biological tissue sample. The image is processed to quantify parameters, each parameter describing a feature of the collagens that is expected to be different for different phenotypes of fibrosis. At least some features are tissue level features that describe macroscopic characteristics of the collagens, morphometric level features that describe morphometric characteristics of the collagens, and texture level features that describe an organization of the collagens. At least some of the plurality of parameters are statistics associated with histograms corresponding to distributions of the associated parameters across at least some of the digital image. At least some of the plurality of parameters are combined to obtain one or more composite scores that quantify a phenotype of fibrosis for the biological tissue sample.

The present invention relates to a method for displaying an easy-to-understand medical image, comprising the steps of: a. obtaining a medical image, b. identifying at least one feature on the image of step (a), c. generating at least one mask highlighting the at least one feature, d. displaying at least one easy-to-understand medical image including at least one mask on which the at least one feature identified in step (b) is highlighted.

The present invention relates to the technical field of medical image processing and, in particular, to a liver boundary identification method and a system. The method includes: obtaining liver tissue information of a liver tissue to be identified; identifying a liver tissue boundary in the liver tissue information according to a feature of the liver tissue corresponding to the liver tissue information and a feature of the liver tissue boundary corresponding to the liver tissue information using an image processing technology or a signal processing technology; and outputting position information of the identified liver tissue boundary. By using the disclosed method, the liver tissue boundary can be identified automatically, the efficiency of identifying the liver boundary can be improved, and automatic positioning of the liver boundary can thus be achieved.

A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

An image processing apparatus includes a first extraction unit configured to extract a first target region from an image using a trained classifier, a setting unit configured to set region information to be used in a graph cut segmentation method based on a first extraction result including the first target region, a second extraction unit configured to extract a second target region using the graph cut segmentation method based on the set region information, and a generation unit configured to generate a ground truth image corresponding to the image based on a second extraction result including the second target region.

Disclosed herein are an apparatus for automatic ultrasound diagnosis of liver steatosis using feature points in an ultrasound image, which can automatically determine a grade of liver steatosis, which is difficult to determine visually, through extraction from an image acquired by medical imaging, and a remote medical diagnosis method using the same.

The present invention relates to a method for classification of an organ in a tomographic image. The method comprises the steps of receiving (102) a 3-dimensional anatomical tomographic target image comprising a water image data set and a fat image data set, each with a plurality of volume elements, providing (104) a prototype image comprising a 3-dimensional image data set with a plurality of volume elements, wherein a sub-set of the volume elements are given an organ label, transforming (106) the prototype image by applying a deformation field onto the volume elements of the prototype image such that each labeled volume element for a current organ is determined to be equivalent to a location for a volume element in a corresponding organ in the target image, and transferring (108) the labels of the labeled volume elements of the prototype image to corresponding volume elements of the target image.

A second registration unit performs registration between an intraoperative live view obtained by an image obtaining unit and an associated image obtained by an associated image obtaining unit. At this time, the second registration unit extracts a plurality of feature points corresponding to one another from a registered intraoperative image registered with the associated image and a newly obtained intraoperative image, sets priority levels on the feature points corresponding to one another based on the associated image, obtains positional information indicating a relative positional difference between the registered intraoperative image and the newly obtained intraoperative image based on the feature points with the priority levels set thereon, and performs registration between the associated image and the newly obtained intraoperative image based on the positional information.

A method and system for registering pre-operative images and intra-operative images using biomechanical simulations is disclosed. A pre-operative image is initially registered to an intra-operative image by estimating deformations of one or more segmented anatomical structures in the pre-operative image, such as the liver, surrounding tissue, and the abdominal wall, using biomechanical gas insufflation model constrained. The initially registered pre-operative image is then refined using diffeomorphic non-rigid refinement.