Deep learning to overcome Zernike phase‐contrast nanoCT artifacts for automated micro‐nano porosity segmentation in bone

Bone material contains a hierarchical network of micro‐ and nano‐cavities and channels, known as the lacuna‐canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer‐sized lacunae, voids that house osteocytes, and submicrometer‐sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X‐ray Zernike phase‐contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact‐laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade‐off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U‐Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase‐contrast transmission X‐ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human‐identified ground‐truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non‐linear artifacts that degrade image quality and hinder feature identification. A deep‐learning processing approach is proposed to assess in three dimensions the micro‐ and nano‐porosity in bone imaged by Zernike nano‐computed tomography.