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…
医学图像分割对通用模型的需求日益增长:模型经过一次训练后便可应用于各种分割任务。此类模型不仅在模型容量方面表现出更高的通用性,而且还可能在不同任务中产生更一致的结果。然而,由于自然图像和医学图像之间存在显著差异,分割基础模型(例如 SAM7 )在医学图像分割中的适用性仍然有限。本质上,SAM 是一种可提示的分割...
这种通用性的缺乏对这些模型在临床实践中的广泛应用构成了实质性的障碍。相比之下,自然图像分割领域的最新进展见证了分割基础模型的出现,例如分割任何模型(Segment Anything Model,SAM)和分割所有地方的多模态提示(Multi-modal prompt all at once),在各种分割任务中展示了卓越的多功能性和性能。 医学图像分割中对通用...
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
“Segment Anything in Medical Images(MedSAM)”是一篇将SAM扩展到医学图像分割的论文。该论文构建了一个大规模的医学图像数据集,包含了11种不同模态的超过20万个Mask,并提出了一种简单的微调方法来适应SAM到通用的医学图像分割。该论文...
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...
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 ...
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)...