Multi-view representation learning for segmentation of abnormal tissues in medical images applied to multiple sclerosis lesion delineation

Abstract

Automated segmentation of abnormal tissues in medical images assists both physicians and medical researchers in the process of diseases diagnostic and research activities respectively. Intelligent techniques of automated segmentation are gaining more popularity in contrast to non-intelligent ones. In these techniques, quality representation of pixel/voxels by considering multiple natural and artificial views which exist in medical images increases segmentation accuracy. The proposed method for segmentation of abnormal tissues in medical images is based on multi-view representation with six phases of pre-processing, view generation, representation generation, classification, post-processing, and evaluation. In the representation phase, raw data of medical images are represented based on the modes of variation or clusters exist in the original multi-view feature space. Quantitative results of the experiment demonstrate representations generated via the proposed method are effective especially when the Random Forest classifier is employed. DSC of 0.72 for a subject shows that the results are promising. This study shows cluster based representation of raw pixel/voxels of multiple views are effective in supervised segmentation of abnormal tissues.