Letting Osteocytes Teach SR-MicroCT Bone Lacunae Segmentation: A Feature Variation Distillation Method via Diffusion Denoising

Synchrotron Radiation micro-Computed Tomography (SR-microCT) is a promising imaging technique for osteocyte-lacunar bone pathophysiology study. However, acquiring them costs more than histopathology, thus requiring multi-modal approaches to enrich limited/costly data with complementary information. Nevertheless, paired modalities are rarely available in clinical settings. To overcome these problems, we present a novel histopathology-enhanced disease-aware distillation model for bone microstructure segmentation from SR-microCTs. Our method uses unpaired histopathology images to emphasize lacunae morphology during SR-microCT image training while avoiding the need for histopathologies during testing. Specifically, we leverage denoising diffusion to eliminate the noisy information within the student and distill valuable information effectively. On top of this, a feature variation distillation method pushes the student to learn intra-class semantic variations similar to the teacher, improving label co-occurrence information learning. Experimental results on clinical and public microscopy datasets demonstrate superior performance over single-, multi-modal, and state-of-the-art distillation methods for image segmentation.