Abstract: Accurate pathological image segmentation is crucial for the clinical diagnosis of breast cancer. However, existing methods of pathological segmentation face challenges due to the variability ...

Aiming at the problems of intensity inhomogeneity, boundary blurring and noise interference in the segmentation of three-dimensional volume data (such as medical images and industrial CT data). In ...

How do you reliably find, segment and track every instance of any concept across large image and video collections using simple prompts? Meta AI Team has just released Meta Segment Anything Model 3, ...

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 ...

Abstract: Accurate porosity evaluation of wound dressings is essential for assessing their effectiveness in fluid absorption and tissue healing. This study presents a comparative analysis between ...

A new artificial intelligence (AI) tool could make it much easier-and cheaper-for doctors and researchers to train medical imaging software, even when only a small number of patient scans are ...

To address the trade-off between segmentation performance and model lightweighting in computer-aided skin lesion segmentation, this paper proposes a lightweight network architecture, Multi-Conv ...

VAN-GAN: Vessel Segmentation Generative Adversarial Network. A tool to segment 3D vascular networks without paired training data. Code repository for training a brain tumour U-Net 3D image ...

Code repository for training a brain tumour U-Net 3D image segmentation model using the 'Task1 Brain Tumour' medical segmentation decathlon challenge dataset.