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.

In one example a computing device can include a processing resource and a non-transitory computer readable medium storing instructions executable by the processing resource to: identify when a first element overlaps a second element within a first image, identify an overlap location based on a shape search within the first image to identify an outline of the first element and the second element, and generate a second image that includes: the first element without the second element, and the second element with a cropped portion that corresponds to the overlap location.

A method for identifying mineral pore types in mud shale includes: determining an inorganic mineral pore image and a kerogen region image of a mud shale Scanning Electron Microscopy (SEM) gray-scale image; performing an expansion operation on the inorganic mineral pore image to obtain an expanded inorganic mineral pore image; comparing the inorganic mineral pore image with the expanded inorganic mineral pore image, and determining an extra region in the expanded inorganic mineral pore image as an expansion region; collecting statistics about the number of pixel points of a siliceous mineral, a calcareous mineral, and a clay mineral; calculating the proportion of each mineral according to the number of pixel points of the minerals; drawing a mineral pore triangular image chart according to the proportions of minerals; and determining the mineral type corresponding to the pores in the inorganic mineral pore image according to the mineral pore triangular image chart.

Embodiments generate an early stage NSCLC recurrence prognosis, and predict added benefit of adjuvant chemotherapy. Embodiments include processors configured to access a radiological image of a region of tissue demonstrating early stage NSCLC; segment a tumor represented in the radiological image; define a peritumoral region based on a morphological dilation of a boundary of the tumor; extract a radiomic signature that includes a set of tumoral radiomic features extracted from the tumoral region, and a set of peritumoral radiomic features extracted from the peritumoral region, based on a continuous time to event data; compute a radiomic score based on the radiomic signature; compute a probability of added benefit of adjuvant chemotherapy based on the radiomic score; and generate an NSCLC recurrence prognosis based on the radiomic score. Embodiments may display the radiomic score, or generate a personalized treatment plan based on the radiomic score.

An imaging controller is provided for segmenting instances from depth images including objects to be manipulated by a robot. The imaging controller includes an input interface configured to receive a depth image that includes objects, a memory configured to store instructions and a neural network trained to segment instances from the objects in the depth image, and a processor, coupled with the memory, configured to perform the instructions to segment a pickable instance using the trained neural network. The instructions include steps of selecting a tallest point in the depth image, defining a region using a shape such that the region surrounds the tallest point, sampling points in the region of the depth image, computing depth-geodesics between the tallest point and the sampled points, submitting the depth-geodesics to the neural network to segment the pickable instance among instances of the objects in the depth image, and an output interface configured to output a geometrical feature of the pickable instance to a manipulator controller of the robot.

Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

A mechanism is provided for seed relabeling for seed-based slice-wise lesion segmentation. The mechanism receives a lesion mask for a three-dimensional medical image volume. The lesion mask corresponds to detected lesions in the medical image volume and wherein each detected lesion has a lesion contour. The mechanism generates a distance map for a given two-dimensional slice in the medical image volume based on the lesion mask. The distance map comprises a distance to a lesion contour for each voxel of the given two-dimensional slice. The mechanism performs local maxima identification to select a set of local maxima from the distance map such that each local maximum has a value greater than its immediate neighbor points. The mechanism performs seed relabeling based on the distance map and the set of local maxima to generate a set of seeds. Each seed represents a center of a distinct component of a lesion contour. The mechanism performs image segmentation on the lesion mask based on the set of seeds to form a split lesion mask.

A mechanism is provided in a data processing system for refining lesion contours with combined active contour and inpainting. The mechanism receives an initial segmented medical image having organ tissue including a set of object contours and a contour to be refined. The mechanism inpaints object voxels inside all contours of the set. The mechanism calculates an updated contour around the contour to be refined based on the in-painted object voxels to form an updated segmented medical image. The mechanism determines whether the updated segmented medical image is improved compared to the initial segmented medical image. The mechanism keeps the updated segmented medical image responsive to the updated segmented medical image being improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for learning template representation libraries. In one aspect, a method includes obtaining an image depicting a physical environment, where the environment includes a given physical object. When possible, a position of the given object in the environment is inferred based on a template representation library using template matching techniques. In response to determining that the position of the given object in the environment cannot be inferred based on the template representation library using template matching techniques, the template representation library is automatically augmented with new template representations.

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.