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2408.06170
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Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging
12 August 2024
Yosuke Yamagishi
S. Hanaoka
Tomohiro Kikuchi
Takahiro Nakao
Yuta Nakamura
Y. Nomura
S. Miki
T. Yoshikawa
O. Abe
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Papers citing
"Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging"
6 / 6 papers shown
Title
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models
Yankai Jiang
Peng Zhang
Ke Wang
Yuan Tian
Hai Lin
Xinyu Wang
MedIm
133
0
0
05 May 2025
SAM2 for Image and Video Segmentation: A Comprehensive Survey
Zhang Jiaxing
Tang Hao
VLM
54
0
0
17 Mar 2025
SurgiSAM2: Fine-tuning a foundational model for surgical video anatomy segmentation and detection
Devanish N. Kamtam
Joseph B. Shrager
Satya Deepya Malla
Xiaohan Wang
Nicole Lin
Juan J. Cardona
Serena Yeung-Levy
Clarence Hu
VLM
55
0
0
05 Mar 2025
Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment
Yankai Jiang
Wenhui Lei
Xiaofan Zhang
S. Zhang
MedIm
40
2
0
21 Oct 2024
On Efficient Variants of Segment Anything Model: A Survey
Xiaorui Sun
Xiaozhong Liu
H. Shen
Xiaofeng Zhu
Ping Hu
VLM
51
4
0
07 Oct 2024
Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey
Yichi Zhang
Zhenrong Shen
VLM
32
12
0
23 Aug 2024
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