值得注意的是,MedSAM在一些不可见的模式(例如腹部T1 Inphase和Outphase)中也取得了更好的性能,超过SAM和专家模型,改进高达10%。图4c给出了四个随机选择的分割示例进行定性评价,结果表明,虽然所有方法都具有处理简单分割目标的能力,但MedSAM在分割具有难以区分边界的挑战性目标方面表现更好,例如MR图像中的宫颈癌(更多...
为了应对这一挑战,我们整理了一个多样化、大规模的医学图像分割数据集,其中包含 1,570,263 个医学图像掩模对,涵盖 10 种成像模式、30 多种癌症类型和多种成像协议(图1和补充表 1-4)。这个大规模数据集使 MedSAM 能够学习丰富的医学图像表示,捕捉不同模式下的广泛解剖结构和病变。图2a概览了数据集中不同医学成...
基本信息标题:Segment anything in medical images中文标题:分割一切医学图像发表年份: 2024年1月期刊/会议: Nature Communications分区: SCI 1区IF:16.6作者: Jun Ma; Bo Wang(一作;通讯)单位:加拿大多伦多…
“SAM Fails to Segment Anything? – SAM-Adapter: Adapting SAM in Few-shot Learning”是一篇针对SAM在某些分割任务中表现不佳的问题,提出了一种基于少样本学习的适配方法的论文。没有对SAM网络进行微调,而是提出了SAM-Adapter,它通...
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability
MedSAM: Segment Anything in Medical Images 目录 前言 SAM 拆解分析 从医学角度理解 SAM 的效用 MedSAM 实验 总结 参考 前言 SAM 是一种在自然图像分割方面取得成功的模型,但在医学图像分割方面表现不佳。MedSAM 首次尝试将 SAM 的成功扩展到医学图像,并成为用于分割各种医学图像的通用工具。为了开发 MedSAM,首先...
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability
Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical data remains unclear. In this work, we first present a comprehensive benchmarking of the Segment Anything Model 2 (SAM2)...
However, the viability of its application to medical image segmentation remains uncertain, given the substantial distinctions between natural and medical images. In this work, we provide a comprehensive overview of recent endeavors aimed at extending the efficacy of SAM to medical image segmentation ...
Medical image segmentation Performance Segmentation models Simple++ Ultrasound images 摘要 The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Thanks to its impressive capabilities in all-round segmentation tasks and its prompt-based interface, SAM has spark...