Article Dans Une Revue International Journal of Computer Assisted Radiology and Surgery Année : 2025

Landmark-free Automatic Digital Twin Registration in Robot-assisted Partial Nephrectomy using a Generic End-to-end Model

Résumé

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 demonstrate 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 1 and resource constraints required in clinical practice, making it a promising tool for Augmented Reality-assisted Partial Nephrectomy.

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hal-05156131 , version 1 (10-07-2025)

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  • HAL Id : hal-05156131 , version 1

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Kilian Chandelon, Alice Pitout, Mathieu Souchaud, Julie Desternes, Gaëlle Margue, et al.. Landmark-free Automatic Digital Twin Registration in Robot-assisted Partial Nephrectomy using a Generic End-to-end Model. International Journal of Computer Assisted Radiology and Surgery, In press. ⟨hal-05156131⟩
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