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

arXiv: https://arxiv.org/abs/2007.02096 · HTML: https://mlsanity.com/paper/2007.02096/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks](https://mlsanity.com/paper/2311.11969/) (2023)
- [Segment Anything Model for Medical Images?](https://mlsanity.com/paper/2304.14660/) (2023)
- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results](https://mlsanity.com/paper/2112.10074/) (2021)
- [MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling](https://mlsanity.com/paper/2303.09373/) (2023)
- [Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis](https://mlsanity.com/paper/2206.04281/) (2022)
- [Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration](https://mlsanity.com/paper/2402.18933/) (2024)
- [SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization](https://mlsanity.com/paper/2205.04329/) (2022)
- [Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis](https://mlsanity.com/paper/2505.00627/) (2025)
- [Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data](https://mlsanity.com/paper/2211.02400/) (2022)
- [Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework](https://mlsanity.com/paper/2402.06875/) (2024)
- [HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation](https://mlsanity.com/paper/2304.04614/) (2023)
- [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026)
- [Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan](https://mlsanity.com/paper/2308.06774/) (2023)
- [Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping](https://mlsanity.com/paper/2203.04299/) (2022)
- [Finding the Most Transferable Tasks for Brain Image Segmentation](https://mlsanity.com/paper/2301.00934/) (2023)
- [FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation](https://mlsanity.com/paper/2304.11557/) (2023)
- [DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data](https://mlsanity.com/paper/2310.13349/) (2023)
- [VINNA for Neonates -- Orientation Independence through Latent Augmentations](https://mlsanity.com/paper/2311.17546/) (2023)
- [Enhancing Generalized Fetal Brain MRI Segmentation using A Cascade Network with Depth-wise Separable Convolution and Attention Mechanism](https://mlsanity.com/paper/2405.15205/) (2024)
- [Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially](https://mlsanity.com/paper/2502.01000/) (2025)
- [Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation](https://mlsanity.com/paper/2502.02340/) (2025)
- [A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation](https://mlsanity.com/paper/2602.03292/) (2026)
- [Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation](https://mlsanity.com/paper/2211.08783/) (2022)
- [The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data](https://mlsanity.com/paper/2306.10484/) (2023)
- [A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading](https://mlsanity.com/paper/2307.09886/) (2023)
- [Automatic 8-tissue Segmentation for 6-month Infant Brains](https://mlsanity.com/paper/2408.15198/) (2024)
- [Selecting the Best Sequential Transfer Path for Medical Image Segmentation with Limited Labeled Data](https://mlsanity.com/paper/2410.06892/) (2024)

## Abstract (arXiv)

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.