Segmentation method of multiple sclerosis lesions based on 3D‐CNN networks

Abstract

Histopathology image segmentation is an important area in the field of computer aided diagnosis using image processing. The segmentation of Multiple sclerosis (MS) lesions from MR images can establish the basis for subsequent lesion reconstruction, volume estimation, and course evaluation. This study proposes a method for automatically segmenting MS lesions based on 3D convolutional neural network (CNN). The method is divided into two stages, each of which includes two convolution layers and two pooling layers. The alternative lesion voxels are selected in the first stage, while in the second stage, the final lesion voxels are segmented from the lesion voxels which are obtained in the first stage by restricting the conditions. The method has been tested on the MICCAI 2008 and 2016 datasets and compared to the other baseline methods. The experiment results show that the method has better performance than the other baseline methods on different evaluation indicators, including dice similarity coefficient, absolute difference in lesion volume, true positive rate, false positive rate, and predictive positivity value.