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.
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.
摘要：
背景：过敏性鼻炎是一个广泛的健康问题，传统治疗往往被证明是痛苦和无效的。针对翼腭窝的针刺被证明是有效的，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的安全性和精确性，我们引入了一个基于深度学习的模型来细化翼腭窝的分割。我们的模型使用DenseASPP扩展了U-Net框架，并集成了一种注意力机制，以提高翼腭窝的定位和分割精度。
结果：该模型实现了93.89%的骰子相似系数和2.53mm的95%Hausdorff距离，具有显著的精度。值得注意的是,它只使用1.98M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，为翼腭窝辅助穿刺提供可靠的指导依据。
方法：为了提高针对翼腭窝的安全性和精确性，我们引入了一个基于深度学习的模型来细化翼腭窝的分割。我们的模型使用DenseASPP扩展了U-Net框架，并集成了一种注意力机制，以提高翼腭窝的定位和分割精度。
结果：该模型实现了93.89%的骰子相似系数和2.53mm的95%Hausdorff距离，具有显著的精度。值得注意的是,它只使用1.98M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，为翼腭窝辅助穿刺提供可靠的指导依据。
