๐ฅ Accurate visualization of subtle vascular dynamics remains a significant challenge in minimally invasive surgery, where dynamic complexities often limit decision-making reliability. Our paper introduces ๐๐ป๐ฑ๐ผ๐๐ผ๐ป๐๐ฟ๐ผ๐น๐ ๐ฎ๐ด, a framework designed to ๐ฒ๐ป๐ต๐ฎ๐ป๐ฐ๐ฒ ๐๐ฎ๐๐ฐ๐๐น๐ฎ๐ฟ ๐บ๐ผ๐๐ถ๐ผ๐ป ๐๐ถ๐๐ถ๐ฏ๐ถ๐น๐ถ๐๐ in endoscopic videos while preserving surrounding tissue structure. The approach integrates ๐ฃ๐ฒ๐ฟ๐ถ๐ผ๐ฑ๐ถ๐ฐ ๐ฅ๐ฒ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐ฅ๐ฒ๐๐ฒ๐๐๐ถ๐ป๐ด to minimize error accumulation over time and ๐๐ถ๐ฒ๐ฟ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐ฐ๐ฎ๐น ๐ง๐ถ๐๐๐๐ฒ-๐ฎ๐๐ฎ๐ฟ๐ฒ ๐ ๐ฎ๐ด๐ป๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป for adaptive vessel tracking.
๐ To validate robustness, we constructed ๐๐ป๐ฑ๐ผ๐ฉ๐ ๐ ๐ฎ๐ฐ, a benchmark dataset spanning four surgical specialties and diverse intraoperative scenarios. Quantitative metrics and expert surgeon evaluations indicate improved magnification accuracy and image quality compared to existing methods.
๐ค We extend our sincere gratitude to our collaborators across The Chinese University of Hong Kong (An Wang, Mengya Xu, Yiting Chang, Prof Hongliang Ren), The University of Hong Kong (Rulin Zhou), Southern Medical University (่้พ้ฃ, Prof Hao Chen), The First Affiliated Hospital of Wenzhou Medical University (Yiru Ye), Southern University of Science and
Technology (Prof Jiankun Wang), and Singapore General Hospital (Prof Chwee Ming Lim) for their invaluable contributions to this multidisciplinary work.
The paper is available at https://lnkd.in/ggPEFswF
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