Deep learning (DL) enables automated bone segmentation in micro-CT datasets but can struggle to generalize across developmental stages, anatomical regions, and imaging conditions. We present BP-2D-03, ...

Background: 3D medical image segmentation is a cornerstone for quantitative analysis and clinical decision-making in various modalities. However, acquiring high-quality voxel-level annotations is both ...

This repo contains a PyTorch an implementation of different semantic segmentation models for different datasets. Note that when using COCO dataset, 164k version is used per default, if 10k is prefered ...

Abstract: Foundation models for interactive segmentation in 2D natural images and videos have sparked significant interest in building 3D foundation models for medical imaging. However, the domain ...

Semantic segmentation is a core task in computer vision, essential for applications requiring detailed scene understanding, such as medical imaging, precision agriculture, and remote sensing. Recent ...

Less than four months after unveiling its video-focused Segment Anything Model 2, Meta has released SAM 3 and SAM 3D, immediately deploying the advanced computer vision models into consumer products ...

Meta Platforms Inc. today is expanding its suite of open-source Segment Anything computer vision models with the release of SAM 3 and SAM 3D, introducing enhanced object recognition and ...

We’re introducing SAM 3 and SAM 3D, the newest additions to our Segment Anything Collection, which advance AI understanding of the visual world. SAM 3 enables detection and tracking of objects in ...

Dividing or segmenting a market is key for any marketer. By knowing the different types of potential customers we have we can better deliver a product or service that is tailored to them. In ...