A Multimodal Transfer Learning Approach for Histopathology and SR-microCT Low-Data Regimes Image Segmentation.

Osteocyte-lacunar bone structures are a discerning marker for bone pathophysiology, given their geometric alterations observed during aging and diseases. Deep Learning (DL) image analysis has showcased the potential to comprehend bone health associated with their mechanisms. However, DL examination requires labeled and multimodal datasets, which is arduous with high-dimensional images. Within this context, we propose a method for segmenting osteocytes and lacunae in human bone histopathology and Synchrotron Radiation micro-Computed Tomography (SR-microCT) images, employing a deep U-Net in an intra-domain and multimodal transfer learning setting with a limited number of training images. Our strategy allows achieving 63.92±4.69 and 63.94±4.05 Dice Similarity Coefficient (DSC) osteocytes and lacunae segmentation, while up to 20.38 and 5.86 average DSC improvements over selected baselines even if 44× smaller datasets are employed for training.Clinical relevance-The proposed method analyzes bone histopathologies and SR-microCT images in a multimodal and low-data setting, easing the bone microscale investigations while supporting the study of osteocyte-lacunar pathophysiology.