A computer can operated, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.

A computer can operated, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.

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.

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.

Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.

A tree crown extraction method based on UAV multi-source remote sensing includes: obtaining a visible light image and LIDAR point clouds, taking a digital orthophoto map (DOM) and the LIDAR point clouds as data sources, using a method of watershed segmentation and object-oriented multi-scale segmentation to extract single tree crown information under different canopy densities. The object-oriented multi-scale segmentation method is used to extract crown and non-crown areas, and a tree crown distribution range is extracted with the crown area as a mask; a preliminary segmentation result of single tree crown is obtained by the watershed segmentation method based on a canopy height model; a brightness value of DOM is taken as a feature, the crown area of the DOM is performed secondary segmentation based on a crown boundary to obtain an optimized single tree crown boundary information, which greatly increases the accuracy of remote sensing tree crown extraction.

A tree crown extraction method based on UAV multi-source remote sensing includes: obtaining a visible light image and LIDAR point clouds, taking a digital orthophoto map (DOM) and the LIDAR point clouds as data sources, using a method of watershed segmentation and object-oriented multi-scale segmentation to extract single tree crown information under different canopy densities. The object-oriented multi-scale segmentation method is used to extract crown and non-crown areas, and a tree crown distribution range is extracted with the crown area as a mask; a preliminary segmentation result of single tree crown is obtained by the watershed segmentation method based on a canopy height model; a brightness value of DOM is taken as a feature, the crown area of the DOM is performed secondary segmentation based on a crown boundary to obtain an optimized single tree crown boundary information, which greatly increases the accuracy of remote sensing tree crown extraction.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.