International journal of computer assisted radiology and surgery · 2025
Landmark-free automatic digital twin registration in robot-assisted partial nephrectomy using a generic end-to-end model.
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.
Chandelon Kilian, Pitout Alice, Souchaud Mathieu, Desternes Julie, Margue Gaëlle, Peyras Julien, Bourdel Nicolas, Bernhard Jean-Christophe, Bartoli Adrien
Аннотация
PURPOSE: Augmented Reality in Minimally Invasive Surgery has made tremendous progress in organs including the liver and the uterus. The core problem of Augmented Reality is registration, where a preoperative patient's geometric digital twin must be aligned with the image of the surgical camera. The case of the kidney is yet unresolved, owing to the absence of anatomical landmarks visible in both the patient's digital twin and the surgical images.
METHODS: We propose a landmark-free approach to registration, which is particularly well-adapted to the kidney. The approach involves a generic kidney model and an end-to-end neural network, which we train with a proposed dataset to regress the registration directly from a surgical RGB image.
RESULTS: Experimental evaluation across four clinical cases demonstrates strong concordance with expert-labelled registration, despite anatomical and motion variability. The proposed method achieved an average tumour contour alignment error of 7.3 ± 4.1 mm in 9.4 ± 0.2 ms.
CONCLUSION: This landmark-free registration approach meets the accuracy, speed and resource constraints required in clinical practice, making it a promising tool for Augmented Reality-Assisted Partial Nephrectomy.
METHODS: We propose a landmark-free approach to registration, which is particularly well-adapted to the kidney. The approach involves a generic kidney model and an end-to-end neural network, which we train with a proposed dataset to regress the registration directly from a surgical RGB image.
RESULTS: Experimental evaluation across four clinical cases demonstrates strong concordance with expert-labelled registration, despite anatomical and motion variability. The proposed method achieved an average tumour contour alignment error of 7.3 ± 4.1 mm in 9.4 ± 0.2 ms.
CONCLUSION: This landmark-free registration approach meets the accuracy, speed and resource constraints required in clinical practice, making it a promising tool for Augmented Reality-Assisted Partial Nephrectomy.