Cross-Modality Brain Structures Image Segmentation for the Radiotherapy Target Definition and Plan Optimization

This paper summarizes results of the International Challenge “Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images”, ABCs, organized in conjunction with the MICCAI 2020 conference. Eighteen segmentation algorithms were trained on a set of 45 CT, T\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_1$$\end{document}1-weighted MR, and T\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_2$$\end{document}2-weighted FLAIR MR post-operative images of glioblastoma and low-grade glioma patients. Manual delineations were provided for the brain structures: falx cerebri, tentorium cerebelli, transverse and sagittal brain sinuses, ventricles, cerebellum (Task 1) and for the brainstem, structures of visual pathway, optic chiasm, optic nerves, and eyes, structures of auditory pathway, cochlea, and lacrimal glands (Task 2). The algorithms were tested on a set of 15 cases and received the final score for predicting segmentation on a separate 15 case image set. Multi-rater delineations with seven raters were obtained for the three cases. The results suggest that neural network based algorithms have become a successful technique of brain structure segmentation, and closely approach human performance in segmenting specific brain structures.