In order to perform quantitative analysis on an object in an image, it is important to accurately identify the object, but when plural objects are in contact with each other, it is potential that a target portion cannot be accurately identified. An image is segmented into a foreground region and a background region, the foreground region being a region in which an object for which quantitative information is to be calculated is shown, and the background region being a region other than the foreground region. With respect to a first object and a second object in contact with each other in the image, a contact point between the first object and the second object is detected based on a region segmentation result output by a segmentation unit. The first object and the second object can be separated by connecting two boundary reference pixels including a first boundary reference pixel that is a pixel in a background region closest to the contact point, and a second boundary reference pixel that is a pixel in a background region in a direction opposite to the first boundary reference pixel across the contact point.

Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation and/or implementing instance segmentation based on partial annotations. In various embodiments, a computing system might receive first and second images, the first image comprising a field of view of a biological sample, while the second image comprises labeling of objects of interest in the biological sample. The computing system might encode, using an encoder, the second image to generate third and fourth encoded images (different from each other) that comprise proximity scores or maps. The computing system might train an AI system to predict objects of interest based at least in part on the third and fourth encoded images. The computing system might generate (using regression) and decode (using a decoder) two or more images based on a new image of a biological sample to predict labeling of objects in the new image.

An image of a physical environment is acquired that comprises a plurality of pixels, each pixel including a two-dimensional pixel location in the image plane and a depth value corresponding to a distance between a region of the physical environment and the image plane. For each pixel, the two dimensional pixel location and the depth value is converted into a corresponding three-dimensional point in the physical environment defined by three coordinate components, each of which has a value in physical units of measurement. A set of edge points is determined within the plurality of three-dimensional points based, at least in part, on the z coordinate component of the plurality of points and a distance map is generated comprising a matrix of cells. For each cell of the distance map, a distance value is assigned representing a distance between the cell and the closest edge point to that cell.

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.

A facility diagnoses AMD in a subject patient. The facility obtains one or more patient images for a subject patient, which depict at least one of the subject patient's eyes. The facility applies an image-based classifier to at least one of the patient images to obtain a first AMD risk score. The facility identifies the macular region of an eye depicted in the patient images, and applies a deep learning-based classifier to the identified macular region to obtain a second AMD risk score. The facility identifies lesions present in an eye depicted in the patient images, and applies a deep learning-based classifier to the identified lesions to obtain a third AMD risk score. The facility combines the first AMD risk score, second AMD risk score, and third AMD risk score to obtain a unified AMD risk score.

The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S500.

A three-dimensional shape of a subject is analyzed by inputting captured images of a depth camera and a visible light camera. There is provided an image processing unit configured to input captured images of the depth camera and the visible light camera, to analyze a three-dimensional shape of the subject. The image processing unit generates a depth map based TSDF space (TSDF Volume) by using a depth map acquired from a captured image of the depth camera, and generates a visible light image based TSDF space by using a captured image of the visible light camera. Moreover, an integrated TSDF space is generated by integration processing on the depth map based TSDF space and the visible light image based TSDF space, and three-dimensional shape analysis processing on the subject is executed using the integrated TSDF space.

Presented herein are systems and methods that allow for vertebral centrums of individual vertebrae to be identified and segmented within a 3D image of a subject (e.g., a CT or microCT image). In certain embodiments, the approaches described herein identify, within a graphical representation of an individual vertebra in a 3D image of a subject, multiple discrete and differentiable regions, one of which corresponds to a vertebral centrum of the individual vertebra. The region corresponding to the vertebral centrum may be automatically or manually (e.g., via a user interaction) classified as such. Identifying vertebral centrums in this manner facilitates streamlined quantitative analysis of 3D images for osteological research, notably, providing a basis for rapid and consistent evaluation of vertebral centrum morphometric attributes.

An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: partition pixel values in a unit of row of an input image into a plurality of sections and allocates threads to the respective sections of the row, the threads being enabled to run in parallel by a processor; calculate, with each of the threads allocated in each row, distances each from a pixel having a certain value in the corresponding section of the row in the input image, and generates a first distance image which stores values indicating the distances; and calculate, with each of the threads allocated in each row, a first boundary value indicating a distance from a pixel having the certain value in another section of each row, by using a calculation result of the first boundary value in the another section of each row.

The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S500.