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Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation

Domen Preložnik, Žiga Špiclin

Abstract

Inter-scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that efficacy of strategies involving modifications or constraints on the latent space appears to be contingent upon the level and/or depth of supervision during model training. In this paper, we therefore propose an unsupervised domain adaptation technique based on self-supervised multi-stage unlearning (SSMSU). Building upon the state-of-the-art segmentation framework nnU-Net, we employ deep supervision at deep encoder stages using domain classifier unlearning, applied sequentially across the deep stages to suppress domain-related latent features. Following self-configurable approach of the nnU-Net, the auxiliary feedback loop implements a self-supervised backpropagation schedule for the unlearning process, since continuous unlearning was found to have a detrimental effect on the main segmentation task. Experiments were carried out on four public datasets for benchmarking white-matter lesion segmentation methods. Five benchmark models and/or strategies, covering passive to active unsupervised domain adaptation, were tested. In comparison, the SSMSU demonstrated the advantage of unlearning by enhancing lesion sensitivity and limiting false detections, which resulted in higher overall segmentation quality in terms of segmentation overlap and relative lesion volume error. The proposed model inputs only the FLAIR modality, which simplifies preprocessing pipelines, eliminates the need for inter-modality registration errors and harmonization, which can introduce variability. Source code is available on https://github.com/Pubec/nnunetv2-unlearning.

Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation

Abstract

Inter-scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that efficacy of strategies involving modifications or constraints on the latent space appears to be contingent upon the level and/or depth of supervision during model training. In this paper, we therefore propose an unsupervised domain adaptation technique based on self-supervised multi-stage unlearning (SSMSU). Building upon the state-of-the-art segmentation framework nnU-Net, we employ deep supervision at deep encoder stages using domain classifier unlearning, applied sequentially across the deep stages to suppress domain-related latent features. Following self-configurable approach of the nnU-Net, the auxiliary feedback loop implements a self-supervised backpropagation schedule for the unlearning process, since continuous unlearning was found to have a detrimental effect on the main segmentation task. Experiments were carried out on four public datasets for benchmarking white-matter lesion segmentation methods. Five benchmark models and/or strategies, covering passive to active unsupervised domain adaptation, were tested. In comparison, the SSMSU demonstrated the advantage of unlearning by enhancing lesion sensitivity and limiting false detections, which resulted in higher overall segmentation quality in terms of segmentation overlap and relative lesion volume error. The proposed model inputs only the FLAIR modality, which simplifies preprocessing pipelines, eliminates the need for inter-modality registration errors and harmonization, which can introduce variability. Source code is available on https://github.com/Pubec/nnunetv2-unlearning.
Paper Structure (19 sections, 10 equations, 8 figures, 4 tables)

This paper contains 19 sections, 10 equations, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work and Contributions
  3. Contributions
  4. Materials and Methods
  5. Datasets
  6. Preprocessing
  7. nnU-Net baseline
  8. nnU-Net with multi-stage unlearning
  9. Self-supervised learning–unlearning schedule
  10. Experiments & Results
  11. Evaluation metrics
  12. Ablation study
  13. Lesion segmentation
  14. Discussion
  15. Self-supervised multi-stage unlearning
  16. ...and 4 more sections

Figures (8)

  • Figure 1: Comparison of MRI scans between different domains.
  • Figure 2: (a) MRI preprocessing and (b) self-supervised multi-stage unlearning with nnU-Net.
  • Figure 3: Ablation study metric values with respect to learn-to-unlearn ratio (LUR=1-1, 2-1, 3-1, 4-1, 5-1), for various unlearning stages: A - from bottleneck, B - bottom three blocks, C - top three blocks, D - entire encoder.
  • Figure 4: DSC with 95% confidence intervals for MSSEG 2016 unseen test set. For notes (*,**) see caption of Table \ref{['table:lesion-segmentation-results-comparison']}.
  • Figure 5: DSC with 95% confidence intervals per source domain of MSSEG unseen test set for nnU-Net SSMSU.
  • ...and 3 more figures