Fully Automatic Segmentation, Identification and Preoperative Planning for Nasal Surgery of Sinuses Using Semi-Supervised Learning and Volumetric Reconstruction

The aim of this study is to develop an automatic segmentation algorithm based on paranasal sinus CT images, which realizes automatic identification and segmentation of the sinus boundary and its inflamed proportions, as well as the reconstruction of normal sinus and inflamed site volumes. Our goal is to overcome the current clinical dilemma of manually calculating the inflammatory sinus volume, which is objective and ineffective. A semi-supervised learning algorithm using pseudo-labels for self-training was proposed to train convolutional neural networks, which consisted of SENet, MobileNet, and ResNet. An aggregate of 175 CT sets was analyzed, 50 of which were from patients who subsequently underwent sinus surgery. A 3D view and volume-based modified Lund-Mackay score were determined and compared with traditional scores. Compared to state-of-the-art networks, our modifications achieved significant improvements in both sinus segmentation and classification, with an average pixel accuracy of 99.67%, an MIoU of 89.75%, and a Dice coefficient of 90.79%. The fully automatic nasal sinus volume reconstruction system was successfully obtained the relevant detailed information by accurately acquiring the nasal sinus contour edges in the CT images. The accuracy of our algorithm has been validated and the results can be effectively applied to actual clinical medicine or forensic research.

Automated summary

The aim of this study is to develop an automatic segmentation algorithm based on paranasal sinus CT images, which realizes automatic identification and segmentation of the sinus boundary and its inflamed proportions, as well as the reconstruction of normal sinus and inflamed site volumes. The algorithm achieved significant improvements in both sinus segmentation and classification.