# Automatic brain tissue segmentation in fetal MRI using convolutional neural networks

arXiv: https://arxiv.org/abs/1906.04713 · HTML: https://mlsanity.com/paper/1906.04713/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [Fetal Brain Tissue Annotation and Segmentation Challenge Results](https://mlsanity.com/paper/2204.09573/) (2022)
- [Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder](https://mlsanity.com/paper/2108.11720/) (2021)
- [Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation](https://mlsanity.com/paper/2107.03846/) (2021)
- [A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation](https://mlsanity.com/paper/2204.02779/) (2022)
- [FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain](https://mlsanity.com/paper/2208.14360/) (2022)
- [Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI](https://mlsanity.com/paper/2108.04175/) (2021)
- [Learning to segment fetal brain tissue from noisy annotations](https://mlsanity.com/paper/2203.14962/) (2022)
- [CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI](https://mlsanity.com/paper/2205.08239/) (2022)
- [Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation](https://mlsanity.com/paper/2111.04737/) (2021)
- [Detailed delineation of the fetal brain in diffusion MRI via multi-task learning](https://mlsanity.com/paper/2409.06716/) (2024)
- [Automatic linear measurements of the fetal brain on MRI with deep neural networks](https://mlsanity.com/paper/2106.08174/) (2021)
- [FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom](https://mlsanity.com/paper/2109.03624/) (2021)
- [Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)](https://mlsanity.com/paper/2601.15759/) (2026)
- [Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI](https://mlsanity.com/paper/2311.07234/) (2023)

## Abstract (arXiv)

MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias field to remove intensity inhomogeneity as a preprocessing step to segmentation, we propose to perform segmentation using a convolutional neural network that exploits images with synthetically introduced intensity inhomogeneity as data augmentation. The method first uses a CNN to extract the intracranial volume. Thereafter, another CNN with the same architecture is employed to segment the extracted volume into seven brain tissue classes: cerebellum, basal ganglia and thalami, ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter and extracerebral cerebrospinal fluid. To make the method applicable to slices showing intensity inhomogeneity artifacts, the training data was augmented by applying a combination of linear gradients with random offsets and orientations to image slices without artifacts.