Segmentation of Sella Turcica in X-ray Image based on U-Net Architecture

Abstract

The computer-aided evaluation of medical imaging and orthodontic examinations requires the segmentation of Sella Turcica for feature extraction using cephalometric X-ray images, which is a novel and complex task. However, x-ray image segmentation is more complicated than CT and MR images due to less association of dense thin white tissue of Sella Turcica that is difficult to distinguish from cephalometric radiographs. This paper presents deep learning methods for completely automated Sella Turcica segmentation from cephalometric X-ray images using a fully connected neural network ("U-Net"). The experiments were performed and analyzed using secondary datasets provided by PGIMER, Chandigarh. The different pre-trained U-Net models (VGG16 and 19, ResNet34, 50, and 152) are used as an encoder to improve the prediction accuracy of the Sella structure. The author compared the IoU score of all the considered pre-trained models and found that VGG19 and ResNet34 have high IoU scores compared to other models, but out of all the considered models, ResNet34 gives good prediction results.