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

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.

References in corpus (5)

• Automated Design of Deep Learning Methods for Biomedical Image Segmentation
• ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
• Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years
• Few-Shot Adversarial Domain Adaptation
• Infant brain MRI segmentation with dilated convolution pyramid downsampling and self-attention

Cited by in corpus (11)

• Segment Anything Model for Medical Images?
• QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
• SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization
• Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
• HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation
• Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data
• Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis
• A Systematic Collection of Medical Image Datasets for Deep Learning
• A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading
• FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation
• DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data