Training an artificial intelligence system to outline every object in a photograph usually demands thousands of painstakingly ...

Medical image segmentation, the task of tracing the exact outlines of lesions, polyps, and blood vessels in clinical scans, has long been dominated by a trade-off. Convolutional networks excel at ...

The EURO-PROBE dataset provides manually created segmentations of the prostate and intraprostatic urethra on axial T2-weighted MRI volumes, complementing the existing LUND-PROBE (LUND Prostate ...

The amount of aerial and satellite imagery captured worldwide continues to grow. Yet, efficiently identifying and labeling features in these images—like roofs, cars, or trees—remain challenging. To ...

Example of segmentation visualization in the CrossSegmentationExplorer system. After selecting the image volume, segmentation models or files are chosen from the drop-down menus. For each selected ...

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images. For ...

Adobe on Thursday launched the latest iteration of its Firefly family of image generation AI models, a model for generating vectors, and a redesigned web app that houses all its AI models, plus some ...

Crop segmentation, the process of identifying crop regions in images, is fundamental to agricultural monitoring tasks such as yield prediction, pest detection, and growth assessment. Traditional ...

AI has upgraded Grok Imagine Image 2.0 with precise image editing, background removal, multi-reference support and Smart ...