Beyond Instrument Motion: Recognizing Tissue Tension Toward Surgical Skill Assessment
초록
Surgical performance assessment in minimally invasive surgery largely relies on manual expert review, making it time-consuming, subjective, and difficult to scale. While existing surgical video understanding methods address tasks such as instrument segmentation, surgical phase recognition, and action recognition, they do not explicitly capture fine-grained tissue handling, a key indicator of surgical quality. To address this gap, we introduce tissue tension recognition, a new clinically motivated video understanding task for laparoscopic and robot-assisted rectal cancer surgery. To support this task, we construct SurgTension, the first expert-annotated tissue tension dataset, providing a benchmark for objective tissue tension recognition. We further propose TensionTRAC, a lightweight trajectory-based framework that models tissue tension from sparse point trajectories. Using a compact trajectory encoder, TensionTRAC achieves competitive performance against strong pretrained video backbones.
저자 (6명)
- Marko Haralovi — LinkedIn 검색
- Zhiqi Miao — LinkedIn 검색
- Alexander Machiel Bont — LinkedIn 검색
- Jiapan Guo — LinkedIn 검색
- Frans van Workum — LinkedIn 검색
- Estefania Talavera — LinkedIn 검색
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