Automatic Segmentation and 3D Reconstruction of Spine Based on FCN and Marching Cubes in CT Volumes

Abstract

The spine is of great significance in the course of radiotherapy. The accurate location of the spine can provide reference for the determination of the tumor target area and the endanger organ in the radiotherapy plan. However, for some low-resolution areas of CT images, traditional methods cannot achieve a good segmentation effect. Due to the lack of data marked by doctors, there are few studies on the use of deep learning methods for segmentation of the spine. We use threshold segmentation and manual labeling methods to make our own data sets. This article combines the Fully Convolutional Neural Network (FCN) and the Marching Cubes (MC) algorithms to automatically segment and reconstruct the spine in the CT images. And we improved the network structure of FCN because FCN finally lost many details in one step down sampling. In the study, we used data from 40 patients, of which 30 were for training and 10 for testing. The final segmentation accuracy of the improved network is over 93%. The experimental results show that this method has a good segmentation effect and can better restore the shape of the spine and ribs. This preliminary result showed that our spine segmentation method had a great potential to reduce human efforts in labeling CT images in radiation therapy.