Domain-randomized deep learning for neuroimage analysis

TL;DR

<3-5 sentence high-level summary>

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.

Figures (6)

• Figure 1: Synthetic training images. The variability intentionally exceeds realistic bounds of medical imaging to encourage deep neural networks to generalize. To realize the full potential of domain randomization, synthesis-driven training generates a new, unseen input image at every iteration.
• Figure 2: Image synthesis steps. First, we sample a previously generated anatomical label map, and randomly move and deform it. Second, we generate a grayscale image by drawing an intensity for each label. Third, a series of randomized image corruptions lead to complex intensity patterns across the image and each anatomical structure. Both rows begin with the same label map.
• Figure 3: Noise generation. Left: Linearly upsampling a random low-resolution image creates smoothly varying "value noise", which has a machine-generated appearance. Center: Gaussian noise, sampled at full resolution and smoothed via convolution, has a more natural appearance but is inefficient for large kernels. Right: Perlin noise---a type of gradient noise---achieves a natural look without convolutions. The intensity at each point is a combination of the dot products of random gradient vectors (red) at the corners of a unit cell and support vectors (black) from the same corners to that point.
• Figure 4: Fractal noise, or pink noise, results from adding noise over a range of spatial frequencies, with a relative weighting $\omega$ inversely proportional to the frequency. The example shown combines Perlin noise octaves sampled with $C \in \{2, 4, 8, 16, 32\}$ control points along each axis.
• Figure 5: Bias field distribution after upsampling. Uniform sampling results in a bounded, symmetric distribution (blue, shifted by 1.5 for comparison). In contrast, applying the exponential function to normally distributed values guarantees a positive field, but the resulting distribution is asymmetric and includes higher values (exceeding 2 for standard deviation $\sigma = 0.33$, red).