Design CNN On Bone Spine Segmention TO Methods 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.