Diagnosis is assisted by acquiring microscopical observation image data while specifying the position, classifying the image data into histological types with the use of AI, and reconstructing the classification result in a whole lesion. There is provided a pathological diagnosis assisting method that can provide an assistance technology which performs a pathological diagnosis efficiently with satisfactory accuracy by HE staining which is usually used by pathologists. Furthermore, there are provided a pathological diagnosis assisting system, a pathological diagnosis assisting program, and a pre-trained model.

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

A blood flow field estimation apparatus is provided, including an estimation unit that uses a learned model obtained in advance by performing machine learning to learn a relationship between organ tissue three-dimensional structure data including image data of a plurality of organ cross-sectional images serving as cross-sectional images of an organ and having each pixel provided with two or more bit depths and image position information serving as information indicating a position of an image reflected on each of the organ cross-sectional images in the organ, and a blood flow field in the organ, and estimates the blood flow field in the organ of an estimation target, based on the organ tissue three-dimensional structure data of the organ of the estimation target, and an output unit that outputs an estimation result of the estimation unit.

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.

An image-based animal object condition identification apparatus includes: a communication module that receives an image of an object; a memory that stores therein a program configured to extract animal condition information from the received image; and a processor that executes the program. The program extracts continuous animal detection information of each object by inputting the received image into an animal detection model that is trained based on learning data composed of animal images and determines predetermined animal condition information for each class of each animal object by inputting the continuous animal detection information of each object into an animal condition identification model.

The present disclosure provides methods, systems, and non-transitory computer-readable media for assessment of disease treatment or progression on a lesion-by-lesion level. The systems and methods are based on measurements of a variety of features including total number of lesions, total number and proportion of lesions regressing or progressing, changes in dimensions of a lesion over time, and uptake values of a molecular imaging agent.

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.

A method for parallel processing a digitally scanned pathology image is performed by a plurality of processors and includes performing, by a first processor, a first operation of generating a first batch from a first set of patches extracted from a digitally scanned pathology image and providing the generated first batch to a second processor, performing, by the first processor, a second operation of generating a second batch from a second set of patches extracted from the digitally scanned pathology image and providing the generated second batch to the second processor, and performing, by the second processor, a third operation of outputting a first analysis result from the first batch by using a machine learning model, with at least part of time frame for the second operation performed by the first processor overlapping at least part of time frame for the third operation performed by the second processor.

An image processing device includes a hardware processor. The hardware processor designates, from one frame image of a dynamic image acquired by dynamic imaging of a movement of a locomotorium, a plurality of regions or points on a structure included in the locomotorium, sets an alignment reference based on the designated regions or points, tracks the designated regions or points in a plurality of frame images of the dynamic image, aligns a line segment connecting the regions or the points to each other in the plurality of frame images based on the alignment reference, and causes a display to display the line segment so as to be superimposed on a representative frame image of the dynamic image.

A system and method for generating a fusion of volumetric images and surface scan images said system comprising: a processor configuring the system to: receive both a volumetric image tooth mesh and surface scan image tooth crown mesh from a same patient, registered to a similar coordinate system; segment by anatomical structure each of the registered meshes that are in common between each of the registered volumetric image tooth mesh and the surface scan tooth crown mesh; and recognize a fusion vertices for each of the segmented volumetric image tooth mesh and segmented surface scan tooth crown mesh for matching the recognized meshes; remove a surface fragment from the matched volumetric image mesh in common with the matched surface scan image mesh for removal from the volumetric image mesh; and fuse the meshes by triangulating the recognized fusion vertices.