3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies

Stephen G Wahlig; Pierre Nedelec; David A Weiss; Jeffrey D Rudie; Leo P Sugrue; Andreas M Rauschecker

doi:10.3389/fnins.2023.1188336

3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies

Front Neurosci. 2023 Oct 27:17:1188336. doi: 10.3389/fnins.2023.1188336. eCollection 2023.

Authors

Stephen G Wahlig¹, Pierre Nedelec¹, David A Weiss¹, Jeffrey D Rudie^{1

2}, Leo P Sugrue¹, Andreas M Rauschecker¹

Affiliations

¹ Center for Intelligent Imaging (ci2), Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
² Department of Radiology, University of California, San Diego, San Diego, CA, United States.

Abstract

Background and purpose: Deep learning algorithms for segmentation of multiple sclerosis (MS) plaques generally require training on large datasets. This manuscript evaluates the effect of transfer learning from segmentation of another pathology to facilitate use of smaller MS-specific training datasets. That is, a model trained for detection of one type of pathology was re-trained to identify MS lesions and active demyelination.

Materials and methods: In this retrospective study using MRI exams from 149 patients spanning 4/18/2014 to 7/8/2021, 3D convolutional neural networks were trained with a variable number of manually-segmented MS studies. Models were trained for FLAIR lesion segmentation at a single timepoint, new FLAIR lesion segmentation comparing two timepoints, and enhancing (actively demyelinating) lesion segmentation on T1 post-contrast imaging. Models were trained either de-novo or fine-tuned with transfer learning applied to a pre-existing model initially trained on non-MS data. Performance was evaluated with lesionwise sensitivity and positive predictive value (PPV).

Results: For single timepoint FLAIR lesion segmentation with 10 training studies, a fine-tuned model demonstrated improved performance [lesionwise sensitivity 0.55 ± 0.02 (mean ± standard error), PPV 0.66 ± 0.02] compared to a de-novo model (sensitivity 0.49 ± 0.02, p = 0.001; PPV 0.32 ± 0.02, p < 0.001). For new lesion segmentation with 30 training studies and their prior comparisons, a fine-tuned model demonstrated similar sensitivity (0.49 ± 0.05) and significantly improved PPV (0.60 ± 0.05) compared to a de-novo model (sensitivity 0.51 ± 0.04, p = 0.437; PPV 0.43 ± 0.04, p = 0.002). For enhancement segmentation with 20 training studies, a fine-tuned model demonstrated significantly improved overall performance (sensitivity 0.74 ± 0.06, PPV 0.69 ± 0.05) compared to a de-novo model (sensitivity 0.44 ± 0.09, p = 0.001; PPV 0.37 ± 0.05, p = 0.001).

Conclusion: By fine-tuning models trained for other disease pathologies with MS-specific data, competitive models identifying existing MS plaques, new MS plaques, and active demyelination can be built with substantially smaller datasets than would otherwise be required to train new models.

Keywords: deep learning; demyelination; multiple sclerosis; segmentation; transfer learning.