Mireia Alenya | Searcharxiv

arXiv2026

Open-source MRI-informed computational model of human cortical folding

The human cerebral cortex, initially smooth, progressively folds during fetal brain development in utero, giving rise to cortical convolutions. Atypical cortical folding patterns can be associated with neurodevelopmental and neurological disorders. To better understand these conditions, it is crucial to first examine the factors governing healthy cortical folding. Computational modeling provides a powerful way for this purpose and has already helped understanding the influence of key biomechanical parameters on the folding pattern. However, most existing models use simplified geometries, limiting calibration and validation with fetal and neonatal brain Magnetic Resonance Imaging (MRI) and neglecting the influence of initial geometry on fold development. On the other hand, simulations on realistic brain geometries introduce additional challenges, including collision handling, fold characterization, and additional computational cost. Furthermore, model parameters are often difficult to interpret, complicating comparison, clinical translation, and calibration. Finally, computational models of cortical folding also remain rarely accessible. In this work, we introduce a novel computational model of cortical folding, developed using the open-source code FEniCS to simulate folding on a whole-brain geometry generated from fetal MRI data. We also propose a modular, interpretable, and scalable simulation framework built around this computational model and openly available to the community. It uses fetal MRI data to generate realistic input brain meshes and estimate key biomechanical parameters such as cortical growth rate. The framework also integrates a spectral metric for cortical surface analysis to optimize folding pattern predictions from an healthy fetal MRI dataset.

physics.bio-ph↗

arXiv2023

Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration

Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities. Traditional deep learning-based automatic segmentation, although effective, requires extensive training data with ground-truth labels, typically produced by clinicians through a time-consuming annotation process. To overcome this challenge, we propose a novel unsupervised segmentation method based on multi-atlas segmentation, that accurately segments multiple tissues without relying on labeled data for training. Our method employs a cascaded deep learning network for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image. This cascaded network can then be used to register multiple annotated images with the image to be segmented, and combine the propagated labels to form a refined segmentation. Our experiments demonstrate that the proposed cascaded architecture outperforms the state-of-the-art registration methods that were tested. Furthermore, the derived segmentation method achieves similar performance and inference time to nnU-Net while only using a small subset of annotated data for the multi-atlas segmentation task and none for training the network. Our pipeline for registration and multi-atlas segmentation is publicly available at https://github.com/ValBcn/CasReg.

cs.CV↗