Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method.

INTRODUCTION

One of the current approaches to improve our understanding of osteoporosis is to study the development of bone microdamage under mechanical loading. The current practice for evaluating bone microdamage is to quantify damage volume from images of bone samples stained with a contrast agent, often composed of toxic heavy metals and requiring long tissue preparation. This work aims to evaluate the potential of linear microcracks detection and segmentation in trabecular bone samples using well-known deep learning models, namely YOLOv4 and Unet, applied on microCT images.

METHODS

Six trabecular bovine bone cylinders underwent compression until ultimate stress and were subsequently imaged with a microCT at a resolution of 1.95 μm. Two of these samples (samples 1 and 2) were then stained using barium sulfate (BaSO) and imaged again. The unstained samples (samples 3-6) were used to train two neural networks YOLOv4 to detect regions with microdamage further combined with Unet to segment the microdamage at the pixel level in the detected regions. Four different model versions of YOLOv4 were compared using the average Intersection over Union (IoU) and the mean average precision (mAP). The performance of Unet was also measured using two segmentation metrics, the Dice Score and the Intersection over Union (IoU). A qualitative comparison was finally done between the deep learning and the contrast agent approaches.

RESULTS

Among the four versions of YOLOv4, the YOLOv4p5 model resulted in the best performance with an average IoU of 45,32% and 51,12% and a mAP of 28.79% and 46.22%, respectively for samples 1 and 2. The segmentation performance of Unet provided better IoU and DICE score on sample 2 compared to sample 1. The poorer performance of the test on sample 1 could be explained by its poorer contrast to noise ratio (CNR). Indeed, sample 1 resulted in a CNR of 7,96, which was worse than the average CNR in the training samples, while sample 2 resulted in a CNR of 10,08. The qualitative comparison between the contrast agent and the deep learning segmentation showed that two different regions were segmented by the two techniques. Deep learning is segmenting the region inside the cracks while the contrast agent segments the region around it or even regions with no visible damage.

CONCLUSION

The combination of YOLOv4 for microdamage detection with Unet for damage segmentation showed a potential for the detection and segmentation of microdamage in trabecular bone. The accuracy of both neural networks achieved in this work is acceptable considering it is their first application in this specific field and the amount of data was limited. Even if the errors from both neural networks are accumulated, the two-steps approach is faster than the semantic segmentation of the whole volume.