Enhancing Bone Marrow Lesion Segmentation Through Dual-Channel Deep Neural Networks and Test-Time Augmentation

Abstract

Bone marrow lesion (BML) volume is an essential biomarker for understanding knee osteoarthritis (KOA). However, automatic BML segmentation remains challenging due to the irregular shapes and indistinct boundaries of these lesions in knee magnetic resonance images (MRI). To improve BML segmentation, this study investigated two established strategies in the specific context of BML segmentation: (1) integrating bone segmentation as an additional output channel in deep neural networks to facilitate BML segmentation, and (2) incorporating test-time augmentation (TTA) to reduce uncertainty during testing. The added bone segmentation channel provides auxiliary anatomical information that may facilitate BML localization. TTA was used to improve boundary alignment and reduce false positives by generating more robust predictions. Multiple State-of-the-Art deep neural networks for segmentation were employed as the baseline models to compare performance before and after implementing the proposed strategies. A 10-fold cross-validation was conducted on a dataset of knee MR scans from 300 participants. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) for overlap accuracy and the 95% Hausdorff Distance (HD95) for boundary alignment. Paired t-tests were used to assess the significance of improvements from the proposed strategies. Both strategies produced improvements in segmentation performance, although the magnitude and statistical significance of the improvements varied across architectures. The DSC improved from 63.1% to 64.8% for Residual U-Net, 64.2% to 65.8% for Swin UNETR, 61.5% to 66.5% for Attention U-Net, and 66.6% to 69.0% for UNet++. These gains were accompanied by improvements in boundary accuracy and reductions in false positives, reflected in lower HD95 values. Comparison with additional medical image segmentation models under the same evaluation framework showed that the dual-channel UNet++ with TTA achieved the highest BML DSC of 69.0%, followed by U-Mamba at 68.6% and nnU-Net at 65.2%, while U-Net + InceptionResNet-v2 achieved 57.7%. These findings support the potential value of dual-channel and TTA strategies for automated BML analysis, while further validation on independent datasets is needed to assess their broader generalizability and clinical utility.