TGSAM-2: Text-Guided Medical Image Segmentation Using Segment Anything Model 2

Yuan, Runtian; Zhou, Ling; Xu, Jilan; Li, Qingqiu; Chen, Mohan; Zhang, Yuejie; Feng, Rui; Zhang, Tao; Gao, Shang

TGSAM-2: Text-Guided Medical Image Segmentation Using Segment Anything Model 2

https://hdl.handle.net/10779/DRO/DU:30326200

conference contribution

posted on 2025-10-10, 04:06 authored by Runtian Yuan, Ling Zhou, Jilan Xu, Qingqiu Li, Mohan Chen, Yuejie Zhang, Rui Feng, Tao Zhang, Shang GaoShang Gao

TGSAM-2: Text-Guided Medical Image Segmentation Using Segment Anything Model 2<p></p>

Funding

Funder: Science and Technology Commission of Shanghai Municipality

Funder: National Natural Science Foundation of China

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DOI - Is version of https://doi.org/10.1007/978-3-032-05127-1_54

Location

Daejeon, South Korea

Open access

No

Language

eng

Volume

15969

Pagination

565-574

Start date

2025-09-23

End date

2025-09-27

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783032051264

Title of proceedings

MICCAI 2025 : Proceedings of the 28th International Conference on Medical Image Computing and Computer Assisted Intervention

Event

Medical Image Computing and Computer Assisted Intervention. Conference (2025 : 28th : Daejeon, South Korea)

Publisher

Springer Nature

Series

Lecture Notes in Computer Science

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Keywords

Bioengineering Biomedical Imaging Eye Disease and Disorders of Vision Generic health relevance

TGSAM-2: Text-Guided Medical Image Segmentation Using Segment Anything Model 2

Funding

Funder: Science and Technology Commission of Shanghai Municipality

Funder: National Natural Science Foundation of China

History

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Open access

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