# Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years

arXiv: https://arxiv.org/abs/2001.03091 · HTML: https://mlsanity.com/paper/2001.03091/

## Key facts

- Cited by 11 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [PTNet: A High-Resolution Infant MRI Synthesizer Based on Transformer](https://mlsanity.com/paper/2105.13993/) (2021)
- [Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI](https://mlsanity.com/paper/2312.03102/) (2023)
- [Boosting Skull-Stripping Performance for Pediatric Brain Images](https://mlsanity.com/paper/2402.16634/) (2024)
- [Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning](https://mlsanity.com/paper/2301.01369/) (2023)
- [VINNA for Neonates -- Orientation Independence through Latent Augmentations](https://mlsanity.com/paper/2311.17546/) (2023)
- [Deep infant brain segmentation from multi-contrast MRI](https://mlsanity.com/paper/2512.05114/) (2025)
- [Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder](https://mlsanity.com/paper/2307.00976/) (2023)
- [Reconstruction of Cortical Surfaces with Spherical Topology from Infant Brain MRI via Recurrent Deformation Learning](https://mlsanity.com/paper/2312.05986/) (2023)
- [NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg](https://mlsanity.com/paper/2508.09757/) (2025)
- [Rewiring Development in Brain Segmentation: Leveraging Adult Brain Priors for Enhancing Infant MRI Segmentation](https://mlsanity.com/paper/2510.09306/) (2025)

## Abstract (arXiv)

The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.