深層学習モデルによる共焦点顕微鏡画像からの骨細胞ネットワークのマッピングは、若年および高齢の骨の識別を成功させることができる

Osteocytes, the most abundant and mechanosensitive cells in bone tissue, play a pivotal role in bone homeostasis and mechano-responsiveness, orchestrating the delicate balance between bone formation and resorption under daily activity. Studying osteocyte connectivity and understanding their intricate arrangement within the lacunar canalicular network is essential for unravelling bone physiology, which is significantly disrupted during ageing. Much work has been carried out to investigate this relationship, often involving high resolution microscopy of discrete fragments of this network, alongside advanced computational modelling of individual cells. However, traditional methods of segmenting and measuring osteocyte connectomics are time-consuming and labour-intensive, often hindered by human subjectivity and limited throughput. In this study, we explored the application of deep learning and computer vision techniques to automate the segmentation and measurement of osteocyte connectomics, enabling more efficient and accurate analysis. For this specific application, once trained, the analysis was completed within 10 seconds, compared to manual segmentation time of 130 hours. We compared a number of state-of-the-art computer vision models (U-Nets and Vision Transformers) to successfully segment the osteocyte network, finding that an Attention U-Net model can accurately segment and measure 81.8% of osteocytes and 42.1% of dendritic processes, when compared to manual labelling. While further development is required, we demonstrated that this degree of accuracy is already sufficient to distinguish between bones of young (2-month-old) and aged (36-month-old) mice, as well as partially capturing the degeneration induced by genetic modification of osteocytes. Comparison of the model predictions with manual measurements showed no significant difference, indicating that, with additional training, such deep learning algorithms could be trained to human-level accuracy when measuring the osteocyte network. By harnessing the power of these advanced technologies, further developments will likely shed light on the complexities of osteocyte networks with ever-increasing efficiency.