A Learning Strategy for Contrast-agnostic MRI Segmentation (2003.01995v3)

Abstract: We present a deep learning strategy that enables, for the first time, contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additional training or fine-tuning for new modalities. Classical Bayesian methods address this segmentation problem with unsupervised intensity models, but require significant computational resources. In contrast, learning-based methods can be fast at test time, but are sensitive to the data available at training. Our proposed learning method, SynthSeg, leverages a set of training segmentations (no intensity images required) to generate synthetic sample images of widely varying contrasts on the fly during training. These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field. Because each mini-batch has a different synthetic contrast, the final network is not biased towards any MRI contrast. We comprehensively evaluate our approach on four datasets comprising over 1,000 subjects and four types of MR contrast. The results show that our approach successfully segments every contrast in the data, performing slightly better than classical Bayesian segmentation, and three orders of magnitude faster. Moreover, even within the same type of MRI contrast, our strategy generalizes significantly better across datasets, compared to training using real images. Finally, we find that synthesizing a broad range of contrasts, even if unrealistic, increases the generalization of the neural network. Our code and model are open source at https://github.com/BBillot/SynthSeg.

• The paper introduces SynthSeg, a novel approach that synthesizes diverse MRI contrasts during training to enable robust segmentation across modalities.
• It leverages a classical generative model that randomly samples parameters to produce varied, synthetic images, avoiding bias from specific imaging contrasts.
• Evaluation on over 1,000 subjects demonstrates that SynthSeg segments brain MRIs three orders of magnitude faster while maintaining accuracy comparable to traditional methods.

An Analytical Examination of "A Learning Strategy for Contrast-agnostic MRI Segmentation"

This paper introduces SynthSeg, an innovative deep learning approach for contrast-agnostic brain MRI segmentation that leverages synthetic data generation. SynthSeg addresses existing challenges in segmentation across variable MRI modalities by using only label maps during training. This eliminates the need for real imaging data, thus avoiding biases towards specific contrasts.

The authors propose a framework wherein training images are synthesized using a classical generative model, similar to those in Bayesian approaches, to simulate a wide spectrum of MRI contrasts. Instead of relying on the actual intensity distributions of MRI scans, the model's parameters are randomly sampled during training, producing images with varied and sometimes unrealistic contrasts. This ensures that the resulting convolutional neural network (CNN) is robust and does not favor any specific modality or pre-processing step.

Evaluation and Results

The authors evaluate their approach on four distinct datasets, totaling over 1,000 subjects and covering various MR contrasts. SynthSeg demonstrates the capability to segment brain MRIs faster than classical Bayesian segmentation by three orders of magnitude. Furthermore, despite bypassing intensity-specific information during training, SynthSeg performs comparably to traditional methods and the supervised T1-specific networks on real-world data from unseen contrasts.

One notable observation is that training with a wide range of synthesized contrasts, even those physically unrealistic, enhances the generalization of the neural network across different datasets and MRI contrasts. Such modality-independent performance makes SynthSeg suitable for integration with automated neuroimaging pipelines.

Implications and Future Directions

This work opens avenues for adopting learning-based segmentation in clinical settings, eliminating the dependency on large annotated MRI datasets of specific contrasts. The ability to generalize without re-training indicates potential scalability across diverse imaging protocols encountered in clinical practice.

Looking forward, this paper sets the groundwork for further exploration in synthetic data-based learning strategies. Future developments could focus on refining the generative model for even broader applicability while maintaining high precision in brain structure delineation. The accessibility provided by the open-source code paves the way for community-driven improvements and adaptations, potentially influencing wider areas within medical imaging segmentation tasks.

In conclusion, SynthSeg's modality-agnostic training approach represents a substantial step toward more versatile, efficient, and widely applicable CNN-based segmentation tools in neuroimaging, marking a significant shift from traditional algorithmic approaches to adaptable and learning-based models.