Rong Ruichen, Denton Kristin, Jin Kevin W, Quan Peiran, Wen Zhuoyu, Kozlitina Julia, Lyon Stephen, Wang Aileen, Wise Carol A, Beutler Bruce, Yang Donghan M, Li Qiwei, Rios Jonathan J, Xiao Guanghua
Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Center for Pediatric Bone Biology and Translational Research, Scottish Rite for Children, Dallas, TX 75219, USA.
Bioengineering (Basel). 2024 Jul 1;11(7):670. doi: 10.3390/bioengineering11070670.
Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R > 0.92, -value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes.
骨骼异常的基因小鼠模型已在识别与人类骨骼疾病相关的表型方面展现出前景。传统上，表型是通过人工检查X光片来评估的，这是一个繁琐且可能容易出错的过程。作为回应，本研究开发了一种基于深度学习的模型，该模型以准确且可重复的方式简化了从X光片中测量小鼠骨骼长度的过程。开发了一种利用带有EfficientNet - B3特征提取主干的关键点区域卷积神经网络（Keypoint R - CNN）算法的骨骼检测和测量管道，以检测小鼠骨骼位置并测量其长度。该管道是利用94张带有每只小鼠骨骼起始和结束位置专家注释的X光图像开发的。我们管道的准确性在一个包含592张图像的独立数据集测试中进行了评估，并在一个先前发表的包含21300张小鼠标X光片的数据集上进一步得到验证。结果表明，我们的模型在测量胫骨和股骨长度方面与人类表现相当（R > 0.92，p值 = 0），并且在测量骨盆长度的精度和一致性方面显著优于人类。此外，该模型提高了基因关联映射结果的精度和一致性，以降低的变异性识别出基因突变与骨骼表型之间的显著关联。这项研究证明了在识别异常骨骼表型的小鼠模型中，自动测量小鼠骨骼长度的可行性和效率。
