FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline (1910.03866v4)

Abstract: Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and sub-cortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 minute) and surface-based thickness analysis (within only around 1h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.

• The paper introduces FastSurfer, a deep learning-based pipeline that accelerates neuroimaging segmentation while maintaining high accuracy.
• It utilizes the FastSurferCNN architecture with competitive dense blocks to segment 95 anatomical classes in under one minute on a GPU.
• Extensive validations demonstrate improved test-retest reliability and sensitivity, making it a robust tool for large-scale neuroimaging studies.

FastSurfer: A Deep Learning Approach for Rapid Neuroimaging Analysis

The paper presents FastSurfer, a comprehensive deep learning-based neuroimaging pipeline that offers a significant improvement in the speed and accuracy of neuroimaging analysis over traditional methods. It is designed to replace the lengthy and computationally demanding processes of existing pipelines like FreeSurfer, providing an alternative that maintains accuracy while being orders of magnitude faster. FastSurfer achieves this through an innovative neural network architecture tailored for efficient anatomical segmentation and morphometric analysis of structural brain MRI scans.

Key Components and Methodology

FastSurfer consists of several critical components that differentiate it from traditional neuroimaging techniques:

1. Advanced Deep Learning Architecture: FastSurfer features a unique neural network architecture, FastSurferCNN, which performs whole-brain segmentation into 95 anatomical classes in less than one minute on a GPU. The network capitalizes on innovative features such as competitive dense blocks and spatial information aggregation to balance memory efficiency with segmentation precision.
2. Whole Brain Segmentation: The network architecture adopts local and global competitive dense blocks alongside competitive skip pathways, thereby ensuring efficient information flow and minimizing the network's memory requirements without sacrificing accuracy.
3. Surface-Based Analysis: Post-segmentation, FastSurfer facilitates cortical surface reconstruction and thickness analysis using an expedited spectral spherical mapping method. This integration bypasses numerous time-consuming traditional steps, such as skull stripping, thereby enhancing pipeline efficiency.
4. Extensive Validation: The pipeline's accuracy and reliability are rigorously validated over multiple unseen datasets, showcasing its generalization capability across different field strengths, acquisition parameters, and clinical settings. Furthermore, the analysis highlights FastSurfer’s increased test-retest reliability and sensitivity to group differences in dementia.

Results and Implications

FastSurfer demonstrates significant improvements in processing time, requiring less than one hour for comprehensive morphological analysis on a CPU, with further parallelization leading to even shorter processing durations. This is particularly crucial for large cohort studies and clinical settings where timely processing is essential.

Key numerical results indicate that FastSurfer achieves higher segmentation accuracy than competing deep learning models, evidenced by superior Dice similarity coefficients and Hausdorff distances. Moreover, the model's ability to generalize to various MRI data types and its high reliability (as indicated by intraclass correlation coefficients) position it as a robust tool for neuroimaging research.

Future Developments

The paper effectively opens avenues for future developments, especially in scaling these techniques to even larger datasets and potentially integrating domain adaptation techniques to further enhance generalization to unseen modalities. Moreover, continued innovation in deep learning architectures may enhance performance and accuracy in complex 3D brain segmentation tasks.

FastSurfer represents a meaningful step forward in neuroimaging analysis, providing a fast, reliable, and accurate alternative to traditional methods. By reducing the computational burden and increasing throughput, FastSurfer facilitates more extensive and scalable neuroimaging studies across diverse research and clinical applications.