Preoperative planning method based on a MOPSO algorithm for robot-assisted cholecystectomy

Yang, Yan; Han, Shuai D.; Sang, Hongqiang; Fen, Liu

doi:10.1007/s11548-021-02547-2

Cited by 2 publications

(1 citation statement)

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“…Furthermore, delimiting anatomical structures is especially critical for robot-assisted endoscopic sinus and skull base surgeries, where precise identification of 3D boundaries is essential [14]. By accurately mapping osseous structures, surgeons can navigate complex anatomical volumes adjacent to the paranasal sinuses more effectively, thus minimising the risks associated with these invasive procedures [15].…”

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confidence: 99%

Automatic semantic segmentation of the osseous structures of the paranasal sinuses

Sun,

Guerrero-López,

Arias-Londoño

et al. 2024

Preprint

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Endoscopic sinus and skull base surgeries require the use of precise neuronavigation techniques, which may take advantage of accurate delimitation of surrounding structures. This delimitation is critical for robotic-assisted surgery procedures to limit volumes of no resection. In this respect, accurate segmentation of the Osseous Structures surrounding the Paranasal Sinuses (OSPS) is a relevant issue to protect critical anatomic structures during these surgeries. Currently, manual segmentation of these structures is a labour-intensive task and requires expertise, often leading to inconsistencies. This is due to the lack of publicly available automatic models specifically tailored for the automatic delineation of the complex OSPS. To address this gap, we introduce an open-source data/model for the segmentation of these complex structures. The initial model was trained on nine complete ex vivo CT scans of the paranasal region and then improved with semi-supervised learning techniques. When tested on an external data set recorded under different conditions and with various scanners, it achieved a DICE score of 94.82±0.9. These results underscore the effectiveness of the model and its potential for broader research applications. By providing both the dataset and the model publicly available, this work aims to catalyse further research that could improve the precision of clinical interventions of endoscopic sinus and skull-based surgeries.

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confidence: 99%