%0 Journal Article
%T Automatic pterygopalatine fossa segmentation and localisation based on DenseASPP.
%A Wang B
%A Shi W
%J Int J Med Robot
%V 20
%N 2
%D 2024 Apr
%M 38654571
%F 2.483
%R 10.1002/rcs.2633
%X <B>BACKGROUND: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>Our deep learning approach yields significant advancements in localising and segmenting the pterygopalatine fossa, providing a reliable basis for guiding pterygopalatine fossa-assisted punctures.