利用方框提示医学分段 Anything 模型进行点监督脑肿瘤分段

X Liu, J Woo, C Ma, J Ouyang, G El Fakhri
{"title":"利用方框提示医学分段 Anything 模型进行点监督脑肿瘤分段","authors":"X Liu, J Woo, C Ma, J Ouyang, G El Fakhri","doi":"10.1109/nss/mic/rtsd57108.2024.10656071","DOIUrl":null,"url":null,"abstract":"<p><p>Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods.</p>","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":"2024 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497479/pdf/","citationCount":"0","resultStr":"{\"title\":\"Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model.\",\"authors\":\"X Liu, J Woo, C Ma, J Ouyang, G El Fakhri\",\"doi\":\"10.1109/nss/mic/rtsd57108.2024.10656071\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods.</p>\",\"PeriodicalId\":73298,\"journal\":{\"name\":\"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium\",\"volume\":\"2024 \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497479/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/nss/mic/rtsd57108.2024.10656071\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/nss/mic/rtsd57108.2024.10656071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/25 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

病变和解剖结构的划分对于图像引导下的介入治疗非常重要。点监督医学影像分割(PSS)在减轻昂贵的专家划线标记方面具有巨大潜力。然而,由于缺乏精确的尺寸和边界指导,点监督医学影像分割的效果往往达不到预期。虽然最近的视觉基础模型,如医学分割任何模型(MedSAM),在边界框提示分割方面取得了重大进展,但它并不能直接利用点注释,而且容易产生语义模糊。在这项初步研究中,我们引入了一个迭代框架,以促进语义感知的点监督 MedSAM。具体来说,语义框-提示生成器(SBPG)模块能够将点输入转化为潜在的伪边界框建议,并通过基于原型的语义相似性对其进行明确的细化。随后,提示引导空间细化(PGSR)模块利用 MedSAM 卓越的泛化能力推断分割掩码,同时更新 SBPG 中的框建议种子。通过充分的迭代,可以逐步提高性能。我们在 BraTS2018 上对整个脑肿瘤的分割进行了评估,结果表明其性能优于传统的 PSS 方法,与盒式监督方法相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Point-supervised Brain Tumor Segmentation with Box-prompted Medical Segment Anything Model.

Delineating lesions and anatomical structure is important for image-guided interventions. Point-supervised medical image segmentation (PSS) has great potential to alleviate costly expert delineation labeling. However, due to the lack of precise size and boundary guidance, the effectiveness of PSS often falls short of expectations. Although recent vision foundational models, such as the medical segment anything model (MedSAM), have made significant advancements in bounding-box-prompted segmentation, it is not straightforward to utilize point annotation, and is prone to semantic ambiguity. In this preliminary study, we introduce an iterative framework to facilitate semantic-aware point-supervised MedSAM. Specifically, the semantic box-prompt generator (SBPG) module has the capacity to convert the point input into potential pseudo bounding box suggestions, which are explicitly refined by the prototype-based semantic similarity. This is then succeeded by a prompt-guided spatial refinement (PGSR) module that harnesses the exceptional generalizability of MedSAM to infer the segmentation mask, which also updates the box proposal seed in SBPG. Performance can be progressively improved with adequate iterations. We conducted an evaluation on BraTS2018 for the segmentation of whole brain tumors and demonstrated its superior performance compared to traditional PSS methods and on par with box-supervised methods.

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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信