arXiv:2306.08609
공식 코드 · Papers with Code
TomoSAM: a 3D Slicer extension using SAM for tomography segmentation
원문 초록
TomoSAM has been developed to integrate the cutting-edge Segment Anything Model (SAM) into 3D Slicer, a highly capable software platform used for 3D image processing and visualization. SAM is a promptable deep learning model that is able to identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process. The source code associated with this article can be found at https://github.com/fsemerar/SlicerTomoSAM
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Autoadaptive Medical Segment Anything Model
Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
Simple is what you need for efficient and accurate medical image segmentation
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.