Automatic pterygopalatine fossa segmentation and localisation based on DenseASPP

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-04-23 DOI:10.1002/rcs.2633

Bing Wang, Weili Shi

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引用次数: 0

Abstract

Background

Allergic rhinitis constitutes a widespread health concern, with traditional treatments often proving to be painful and ineffective. Acupuncture targeting the pterygopalatine fossa proves effective but is complicated due to the intricate nearby anatomy.

Methods

To enhance the safety and precision in targeting the pterygopalatine fossa, we introduce a deep learning-based model to refine the segmentation of the pterygopalatine fossa. Our model expands the U-Net framework with DenseASPP and integrates an attention mechanism for enhanced precision in the localisation and segmentation of the pterygopalatine fossa.

Results

The model achieves Dice Similarity Coefficient of 93.89% and 95% Hausdorff Distance of 2.53 mm with significant precision. Remarkably, it only uses 1.98 M parameters.

Conclusions

Our deep learning approach yields significant advancements in localising and segmenting the pterygopalatine fossa, providing a reliable basis for guiding pterygopalatine fossa-assisted punctures.

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基于 DenseASPP 的翼腭窝自动分割和定位系统

背景过敏性鼻炎是一个普遍存在的健康问题，传统的治疗方法往往痛苦且无效。针对翼腭窝的针灸被证明是有效的，但由于附近复杂的解剖结构而变得复杂。方法为了提高针对翼腭窝针刺的安全性和精确性，我们引入了基于深度学习的模型来完善翼腭窝的分割。我们的模型利用 DenseASPP 扩展了 U-Net 框架，并整合了注意力机制，以提高翼腭窝定位和分割的精确度。结果该模型的骰子相似系数达到 93.89%，95% Hausdorff 距离为 2.53 mm，精确度显著提高。值得注意的是，它只使用了 1.98 M 个参数。结论我们的深度学习方法在定位和分割翼腭窝方面取得了重大进展，为指导翼腭窝辅助穿刺提供了可靠的依据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.