Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

TL;DR

This paper discusses the iSeg-2019 challenge focused on developing infant brain MRI segmentation algorithms that are robust across multiple sites with different imaging protocols, highlighting current methods and future directions.

Contribution

It introduces a multi-site infant brain segmentation challenge and reviews the top methods, addressing the multi-site issue in deep learning-based segmentation.

Findings

Top methods achieved high accuracy in multi-site segmentation

Multi-site dataset revealed limitations of existing algorithms

Discussion on future research directions for multi-site robustness

Abstract

To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. Training/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participating in iSeg-2019. We review the 8 top-ranked teams by detailing their pipelines/implementations, presenting experimental results and evaluating performance in terms of the whole brain, regions of interest, and gyral landmark curves. We also discuss their limitations and possible future directions for the multi-site issue. We hope that the multi-site dataset in iSeg-2019 and this review article will attract more researchers on the multi-site issue.