Domain-randomized deep learning for neuroimage analysis

Abstract--Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with randomized intensities and anatomical content. By generating diverse data from anatomical segmentation maps, the approach enables models to accurately process image types unseen during training, without retraining or fine-tuning. It has demonstrated effectiveness across modalities including MRI, computed tomography, positron emission tomography, and optical coherence tomography, as well as beyond neuroimaging in ultrasound, electron and fluorescence microscopy, and X-ray microtomography. This tutorial paper reviews the principles, implementation, and potential of the synthesis-driven training paradigm. It highlights key benefits, such as improved generalization and resistance to overfitting, while discussing trade-offs such as increased computational demands. Finally, the article explores practical considerations for adopting the technique, aiming to accelerate the development of generalizable tools that make deep learning more accessible to domain experts without extensive computational resources or machine learning knowledge. EUROIMAGING techniques, such as magnetic resonance imaging (MRI), have enabled the study of the human brain in vivo. Alongside advances in acquisition technology, research in neuroimage processing has led to software that automates systematic data analysis, minimizing human effort while improving accuracy and reproducibility [1]. In recent years, deep learning (DL) has been driving the development of a new class of algorithms with unprecedented speed and accuracy, and for a broad range of tasks, deep neural networks have largely replaced classical techniques. However, a key challenge for DL in neuroimaging is small and highly specific datasets. Many studies include only hundreds or even tens of subjects [2], due to factors such as the high cost of data acquisition, multiple modalities competing for scan time, the large size of multi-dimensional data like time-series acquisitions, the low prevalence of certain neurological disorders, and privacy concerns regarding data sharing [3]. Malte Hoffmann (mhoffmann@mgh.harvard.edu) is with the Athinoula A. Martinos Center for Biomedical Imaging and the Departments of Radiology at Harvard Medical School and Massachusetts General Hospital.