TomoSAM：使用SAM进行断层扫描分割的3D切片器扩展

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-06-24 DOI:10.1016/j.softx.2025.102218

Federico Semeraro , Alexandre M. Quintart , Sergio Fraile Izquierdo , Joseph C. Ferguson

{"title":"TomoSAM：使用SAM进行断层扫描分割的3D切片器扩展","authors":"Federico Semeraro , Alexandre M. Quintart , Sergio Fraile Izquierdo , Joseph C. Ferguson","doi":"10.1016/j.softx.2025.102218","DOIUrl":null,"url":null,"abstract":"<div><div>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 <span><span>https://github.com/fsemerar/SlicerTomoSAM</span><svg><path></path></svg></span> (see detailed code metadata).</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102218"},"PeriodicalIF":2.4000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TomoSAM: A 3D Slicer extension using SAM for tomography segmentation\",\"authors\":\"Federico Semeraro , Alexandre M. Quintart , Sergio Fraile Izquierdo , Joseph C. Ferguson\",\"doi\":\"10.1016/j.softx.2025.102218\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 <span><span>https://github.com/fsemerar/SlicerTomoSAM</span><svg><path></path></svg></span> (see detailed code metadata).</div></div>\",\"PeriodicalId\":21905,\"journal\":{\"name\":\"SoftwareX\",\"volume\":\"31 \",\"pages\":\"Article 102218\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoftwareX\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352711025001852\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711025001852","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

引用次数: 0

摘要

TomoSAM的开发是为了将尖端的分段任意模型（SAM）集成到3D切片器中，这是一种用于3D图像处理和可视化的高性能软件平台。SAM是一种快速的深度学习模型，能够识别物体并以零拍摄的方式创建图像蒙版，仅基于几次用户点击。这些工具之间的协同作用有助于从断层扫描或其他成像技术中分割复杂的3D数据集，否则这些数据集需要费力的人工分割过程。与本文相关的源代码可以在https://github.com/fsemerar/SlicerTomoSAM上找到（请参阅详细的代码元数据）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

TomoSAM: A 3D Slicer extension using SAM for tomography segmentation

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 (see detailed code metadata).

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.