Design CNN On Bone Spine Segmention TO Methodes Image Processing

Abstract

This thesis proposes a deep learning approach to bone segmentation in abdominal CNN+PG. Segmentation is a common initial step in medical images analysis, often fundamental for computer-aided detection and diagnosis systems. The extraction of bones in PG is a challenging task, which if done manually by experts requires a time consuming process and that has not today a broadly recognized automatic solution. The method presented is based on a convolutional neural network, inspired by the U-Net and trained end-to-end, that performs a semantic segmentation of the data. The training dataset is made up of 21 abdominal PG+CNN, each one containing between 0 and 255 2D transversal images. Those images are in full resolution, 4*4*50 voxels, and each voxel is classified by the network into one of the following classes: background, femoral bones, hips, sacrum, sternum, spine and ribs. The output is therefore a bone mask where the bones are recognized and divided into six different classes. In the testing dataset, labeled by experts, the best model achieves a Dice coefficient as average of all bone classes of 0.8980. This work demonstrates, to the best of my knowledge for the first time, the feasibility of automatic bone segmentation and classification for PG using a convolutional neural network.