Unpaired Multi-modal Segmentation via Knowledge Distillation
This work addresses the challenge of multi-modal segmentation without requiring co-registered images, which is incremental but beneficial for medical imaging applications.
The paper tackles the problem of unpaired cross-modality image segmentation by proposing a novel learning scheme with a highly compact architecture, achieving superior segmentation accuracy compared to single-modal training and previous multi-modal approaches on cardiac and abdominal organ segmentation tasks.
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model, we introduce a novel loss term inspired by knowledge distillation, by explicitly constraining the KL-divergence of our derived prediction distributions between modalities. We have extensively validated our approach on two multi-class segmentation problems: i) cardiac structure segmentation, and ii) abdominal organ segmentation. Different network settings, i.e., 2D dilated network and 3D U-net, are utilized to investigate our method's general efficacy. Experimental results on both tasks demonstrate that our novel multi-modal learning scheme consistently outperforms single-modal training and previous multi-modal approaches.