# Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates

arXiv: https://arxiv.org/abs/2501.19338 · HTML: https://mlsanity.com/paper/2501.19338/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 13 of 13)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis](https://mlsanity.com/paper/2305.18453/) (2023)
- [GANs for Medical Image Analysis](https://mlsanity.com/paper/1809.06222/) (2018)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset](https://mlsanity.com/paper/2010.15526/) (2020)
- [Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data](https://mlsanity.com/paper/2403.15103/) (2024)
- [SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments](https://mlsanity.com/paper/2206.06127/) (2022)
- [FetMRQC: a robust quality control system for multi-centric fetal brain MRI](https://mlsanity.com/paper/2311.04780/) (2023)
- [Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair](https://mlsanity.com/paper/1911.06542/) (2019)

## Built on by (most cited first, 1 of 1)

- [Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg](https://mlsanity.com/paper/2504.10244/) (2025)

## Abstract (arXiv)

Developing new methods for the automated analysis of clinical fetal and neonatal MRI data is limited by the scarcity of annotated pathological datasets and privacy concerns that often restrict data sharing, hindering the effectiveness of deep learning models. We address this in two ways. First, we introduce Fetal&Neonatal-DDPM, a novel diffusion model framework designed to generate high-quality synthetic pathological fetal and neonatal MRIs from semantic label images. Second, we enhance training data by modifying healthy label images through morphological alterations to simulate conditions such as ventriculomegaly, cerebellar and pontocerebellar hypoplasia, and microcephaly. By leveraging Fetal&Neonatal-DDPM, we synthesize realistic pathological MRIs from these modified pathological label images. Radiologists rated the synthetic MRIs as significantly (p < 0.05) superior in quality and diagnostic value compared to real MRIs, demonstrating features such as blood vessels and choroid plexus, and improved alignment with label annotations. Synthetic pathological data enhanced state-of-the-art nnUNet segmentation performance, particularly for severe ventriculomegaly cases, with the greatest improvements achieved in ventricle segmentation (Dice scores: 0.9253 vs. 0.7317). This study underscores the potential of generative AI as transformative tool for data augmentation, offering improved segmentation performance in pathological cases. This development represents a significant step towards improving analysis and segmentation accuracy in prenatal imaging, and also offers new ways for data anonymization through the generation of pathologic image data.