Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining (2203.01969v3)

Abstract: Retrospective analysis of brain MRI scans acquired in the clinic has the potential to enable neuroimaging studies with sample sizes much larger than those found in research datasets. However, analysing such clinical images "in the wild" is challenging, since subjects are scanned with highly variable protocols (MR contrast, resolution, orientation, etc.). Nevertheless, recent advances in convolutional neural networks (CNNs) and domain randomisation for image segmentation, best represented by the publicly available method SynthSeg, may enable morphometry of clinical MRI at scale. In this work, we first evaluate SynthSeg on an uncurated, heterogeneous dataset of more than 10,000 scans acquired at Massachusetts General Hospital. We show that SynthSeg is generally robust, but frequently falters on scans with low signal-to-noise ratio or poor tissue contrast. Next, we propose SynthSeg+, a novel method that greatly mitigates these problems using a hierarchy of conditional segmentation and denoising CNNs. We show that this method is considerably more robust than SynthSeg, while also outperforming cascaded networks and state-of-the-art segmentation denoising methods. Finally, we apply our approach to a proof-of-concept volumetric study of ageing, where it closely replicates atrophy patterns observed in research studies conducted on high-quality, 1mm, T1-weighted scans. The code and trained model are publicly available at https://github.com/BBillot/SynthSeg.

• The paper introduces SynthSeg+, a three-stage hierarchical CNN that robustly segments brain MRIs without retraining.
• It employs domain randomization to adapt to diverse MRI contrasts and resolutions found in clinical datasets.
• The method achieves a 23.5 Dice point improvement, ensuring semantically consistent and anatomically accurate outputs.

Robust Segmentation of Brain MRI with Hierarchical CNNs

The paper "Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining" addresses key challenges in brain MRI segmentation using clinical datasets, which are often characterized by high variability in imaging protocols. This research leverages convolutional neural networks (CNNs) to improve the robustness and generality of segmentation methods across varying MRI contrasts and resolutions.

Context and Challenge

Neuroimaging with MRI plays a crucial role in studying brain morphology and connectivity. Research typically uses high-quality, prospectively acquired datasets, yet clinical datasets remain underutilized. These clinical datasets are abundant but lack uniformity, as they involve a wide range of MRI protocols, contrasts, and resolutions that complicate automated analysis. Traditional methods such as Bayesian segmentation have limited efficacy due to sensitivity to partial volume effects, particularly with varying resolutions.

Proposed Method: SynthSeg$^{+}$

The primary contribution of this paper is SynthSeg$^{+}$, which introduces a hierarchical architecture consisting of conditional CNNs that require no retraining for different MRI conditions. This method builds on prior work with SynthSeg by incorporating domain randomization to enhance the segmentation of brain MRIs across diverse and "wild" clinical scenarios.

Architectural Design

1. Hierarchical Model: The architecture involves three sequential CNN modules.
   • Initial Segmentation ($S_1$): Provides coarse segmentations of four major tissue classes to ease the segmentation process.
   • Denoiser ($D$): Enhances robustness by correcting potential semantic inconsistencies in initial segmentations.
   • Final Segmentation ($S_2$): Fine-tunes the segmentation leveraging the corrected outputs from the denoiser.
2. Training Strategy: The components are independently trained using a domain randomization technique that generates synthetic examples with varying contrasts and resolutions, forcing the network to become invariant to these changes.
3. Denoising and Correction: Rather than use handcrafted corruption strategies, this work simulates real-world errors using degraded images to emulate realistic failures in segmentation, thereby training the denoiser more effectively.

Results and Implications

The implementation of SynthSeg$^{+}$ on a dataset of 10,520 MRIs from Massachusetts General Hospital demonstrates significant improvement in segmentation robustness compared to current state-of-the-art methods. The approach achieved a substantial performance increase, particularly when handling scans with low signal-to-noise ratios or poor contrast. SynthSeg$^{+}$ excels in minimizing errors and efficiently capturing the morphological nuances of brain structures across varied MRIs.

Key Findings

• Improved Robustness: SynthSeg$^{+}$ showed an impressive improvement of 23.5 Dice points over SynthSeg in challenging segmentation tasks (i.e., scans classified as 'Big Fails').
• Accuracy and Reliability: Unlike other methods, SynthSeg$^{+}$ effectively handles irregular segmentation boundaries, delivering semantically consistent and anatomically correct outputs.

Future Directions

The ability of SynthSeg$^{+}$ to reliably segment brain MRIs without retraining signifies a crucial advancement towards utilizing large-scale clinical datasets for neuroimaging studies. The architectural advancements and training strategies discussed in this paper could extend to other domains in medical imaging that face similar challenges with data heterogeneity. Furthermore, these findings suggest potential developments in automatic diagnosis and prognosis tools, especially with conditions like Alzheimer's where early detection and analysis are pivotal.

By making the code and models publicly accessible, the authors have paved the way for future research in automated neuroimaging, encouraging widespread adoption and adaptation of machine learning in clinical practices and research applications.