Are 2.5D approaches superior to 3D deep networks in whole brain segmentation?

Saikat Roy, David Kügler, Martin Reuter

Keywords: deep 3D convolutional nets, whole brain segmentation, deep ensemble

Abstract: Segmentation of 3D volumes with a large number of labels, small convoluted structures, and lack of contrast between various structural boundaries is a difficult task. While recent methodological advances across many segmentation tasks are dominated by 3D architectures, currently the strongest performing method for whole brain segmentation is FastSurferCNN, a 2.5D approach. To shed light on the nuanced differences between 2.5D and various 3D approaches, we perform a thorough and fair comparison and suggest a spatially-ensembled 3D architecture. Interestingly, we observe training memory intensive 3D segmentation on full-view images does not outperform the 2.5D approach. A shift to training on patches even while evaluating on full-view solves these limitations of both memory and performance limitations at the same time. We demonstrate significant performance improvements over state-of-the-art 3D methods on both Dice Similarity Coefficient and especially average Hausdorff Distance measures across five datasets. Finally, our validation across variations of neurodegenerative disease states and scanner manufacturers, shows we outperform the previously leading 2.5D approach FastSurferCNN demonstrating robust segmentation performance in realistic settings.

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Paper Type: both

Primary Subject Area: Segmentation

Secondary Subject Area: Application: Radiology

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Code And Data:

Code: https://github.com/Deep-MI/3d-neuro-seg
Model files: http://doi.org/10.34730/67dfccf54c75492388f038128aa4c687
Data: We exclusively use publicly available datasets for our training. While we are not allowed to redistribute the datasets, the training / validation / test datasets may be reproduced following the splits provided on github, and reference segmentations with the FreeSurfer pipeline as extensively explained in the paper (Section 2.4 and Table 1 here) and in the FastSurfer paper (https://doi.org/10.1016/j.neuroimage.2020.117012, Section 2.1 and Appendix there) and code https://github.com/Deep-MI/FastSurfer .