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

Area of Science:

• Biomedical Engineering
• Medical Imaging Analysis
• Computational Pathology

Background:

• Osteocyte-lacunar structures are crucial indicators of bone health and disease.
• Deep Learning (DL) shows promise for analyzing bone microarchitecture but requires extensive labeled datasets.
• High-dimensional imaging data presents challenges for traditional DL segmentation methods.

Purpose of the Study:

• To develop and validate a DL method for segmenting osteocytes and lacunae in human bone images.
• To address the limitations of data-intensive DL approaches by utilizing transfer learning.
• To enable accurate bone microscale investigations in multimodal and low-data scenarios.

Main Methods:

• Implementation of a deep U-Net architecture for image segmentation.
• Application of intra-domain and multimodal transfer learning techniques.
• Training and evaluation on human bone histopathology and Synchrotron Radiation micro-Computed Tomography (SR-microCT) datasets with limited samples.

Main Results:

• Achieved Dice Similarity Coefficient (DSC) scores of 63.92±4.69 for osteocyte segmentation and 63.94±4.05 for lacunae segmentation.
• Demonstrated significant improvements in DSC (up to 20.38% and 5.86%) compared to baseline methods.
• Showcased effective performance using datasets that were up to 44 times smaller than typically required.

Conclusions:

• The proposed DL method enables accurate segmentation of osteocyte-lacunar structures in challenging low-data, multimodal imaging settings.
• This approach facilitates microscale bone investigations and supports the study of osteocyte-lacunar pathophysiology.
• The method offers a valuable tool for advancing research in bone diseases and aging.