# Boosting Skull-Stripping Performance for Pediatric Brain Images

arXiv: https://arxiv.org/abs/2402.16634 · HTML: https://mlsanity.com/paper/2402.16634/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets](https://mlsanity.com/paper/2209.02032/) (2022)
- [Anatomy-aware and acquisition-agnostic joint registration with SynthMorph](https://mlsanity.com/paper/2301.11329/) (2023)
- [Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast](https://mlsanity.com/paper/2004.10221/) (2020)
- [Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years](https://mlsanity.com/paper/2001.03091/) (2020)
- [Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks](https://mlsanity.com/paper/2308.07003/) (2023)
- [Fast Infant MRI Skullstripping with Multiview 2D Convolutional Neural Networks](https://mlsanity.com/paper/1904.12101/) (2019)

## Built on by (most cited first, 8 of 8)

- [MultiMorph: On-demand Atlas Construction](https://mlsanity.com/paper/2504.00247/) (2025)
- [Domain-randomized deep learning for neuroimage analysis](https://mlsanity.com/paper/2507.13458/) (2025)
- [Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation](https://mlsanity.com/paper/2411.06842/) (2024)
- [PediDemi -- A Pediatric Demyelinating Lesion Segmentation Dataset](https://mlsanity.com/paper/2508.13239/) (2025)
- [SingleStrip: learning skull-stripping from a single labeled example](https://mlsanity.com/paper/2508.10464/) (2025)
- [Deep infant brain segmentation from multi-contrast MRI](https://mlsanity.com/paper/2512.05114/) (2025)
- [SFL-Net: Source-Factorized Latent Representation Learning for Multi-Contrast MRI to Tau-PET Synthesis](https://mlsanity.com/paper/2602.22545/) (2026)
- [Search Wide, Focus Deep: Automated Fetal Brain Extraction with Sparse Training Data](https://mlsanity.com/paper/2410.20532/) (2024)

## Abstract (arXiv)

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.