International journal of computer assisted radiology and surgery · 2026
Deep learning-based renal artery segmentation and angle estimation for registration of endoscopic images and 3D models in robot-assisted partial nephrectomy.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tsukino Keiji, Kobayashi Satoshi, Takashima Shunsuke, Miyauchi Shoko, Mutaguchi Jun, Tsukahara Shigehiro, Tanegashima Tokiyoshi, Goto Shunsuke, Matsumoto Takashi, Shiota Masaki, Kurazume Ryo, Eto Masatoshi
Аннотация
PURPOSE: Image-guided robot-assisted partial nephrectomy (RAPN) that incorporates three-dimensional (3D) models has improved both oncological and functional outcomes. However, registration between the physical endoscopic image and the 3D virtual model is often performed manually at the kidney level, limiting accuracy and usability. This study aimed to develop a framework built on deep learning to perform renal artery segmentation and angle estimation, enabling autonomous registration in image-guided RAPN.
METHODS: A total of 75 images were extracted from 50 RAPN videos. The renal arteries in these images were manually annotated and used to evaluate a U-Net segmentation model. Segmented masks by the autonomous segmentation model were approximated as ellipsoid to calculate renal artery angles. The model was evaluated using sevenfold cross-validation and further tested on endoscopic images from four unseen clinical cases, verifying its adaptability. The predicted angles were then applied to rotate the corresponding 3D kidney models, demonstrating the feasibility of autonomous registration.
RESULTS: The segmentation model achieved an average Dice similarity coefficient of 0.817 in sevenfold cross-validation. The prediction errors for the renal artery angles of four RAPN cases were RAPN1 = 0.57°, RAPN2 = 3.4°, RAPN3 = 4.9°, and RAPN4 = 4.1°. The required 3D model rotations were computed from the predicted angles: RAPN1 = + 19.7°, RAPN2 = -19.2°, RAPN3 = -28.4°, and RAPN4 = -13.3°.
CONCLUSIONS: These findings demonstrate that deep learning-based segmentation and angle estimation of the renal artery can be performed accurately, providing a foundation for autonomous registration in image-guided RAPN.
METHODS: A total of 75 images were extracted from 50 RAPN videos. The renal arteries in these images were manually annotated and used to evaluate a U-Net segmentation model. Segmented masks by the autonomous segmentation model were approximated as ellipsoid to calculate renal artery angles. The model was evaluated using sevenfold cross-validation and further tested on endoscopic images from four unseen clinical cases, verifying its adaptability. The predicted angles were then applied to rotate the corresponding 3D kidney models, demonstrating the feasibility of autonomous registration.
RESULTS: The segmentation model achieved an average Dice similarity coefficient of 0.817 in sevenfold cross-validation. The prediction errors for the renal artery angles of four RAPN cases were RAPN1 = 0.57°, RAPN2 = 3.4°, RAPN3 = 4.9°, and RAPN4 = 4.1°. The required 3D model rotations were computed from the predicted angles: RAPN1 = + 19.7°, RAPN2 = -19.2°, RAPN3 = -28.4°, and RAPN4 = -13.3°.
CONCLUSIONS: These findings demonstrate that deep learning-based segmentation and angle estimation of the renal artery can be performed accurately, providing a foundation for autonomous registration in image-guided RAPN.