{Reference Type}: Journal Article
{Title}: Automatic pterygopalatine fossa segmentation and localisation based on DenseASPP.
{Author}: Wang B;Shi W;
{Journal}: Int J Med Robot
{Volume}: 20
{Issue}: 2
{Year}: 2024 Apr
{Factor}: 2.483
{DOI}: 10.1002/rcs.2633
{Abstract}: <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.