Lagzouli Amine, Pivonka Peter, Cooper David M L, Sansalone Vittorio, Othmani Alice
School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, Australia.
Univ Paris Est Créteil, Univ Gustave Eiffel, CNRS, UMR 8208, MSME, F-94010, Créteil, France.
Sci Rep. 2025 Mar 13;15(1):8656. doi: 10.1038/s41598-025-92954-1.
Recent advancements in deep learning have significantly enhanced the segmentation of high-resolution microcomputed tomography (µCT) bone scans. In this paper, we present the dual-branch attention-based hybrid network (DBAHNet), a deep learning architecture designed for automatically segmenting the cortical and trabecular compartments in 3D µCT scans of mouse tibiae. DBAHNet's hierarchical structure combines transformers and convolutional neural networks to capture long-range dependencies and local features for improved contextual representation. We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. This evaluation covered variations in resolutions, ages, mouse strains, drug treatments, surgical procedures, and mechanical loading. DBAHNet demonstrated excellent performance, achieving high accuracy, particularly in challenging scenarios with significantly altered bone morphology. The model's robustness and generalization capabilities were rigorously tested under diverse and unseen conditions, confirming its effectiveness in the automated segmentation of high-resolution µCT mouse tibia scans. Our findings highlight DBAHNet's potential to provide reliable and accurate 3D µCT mouse tibia segmentation, thereby enhancing and accelerating preclinical bone studies in drug development. The model and code are available at https://github.com/bigfahma/DBAHNet .
深度学习的最新进展显著提升了高分辨率微型计算机断层扫描（µCT）骨扫描的分割效果。在本文中，我们提出了基于双分支注意力的混合网络（DBAHNet），这是一种深度学习架构，旨在自动分割小鼠胫骨的三维µCT扫描中的皮质骨和小梁骨区域。DBAHNet的分层结构结合了变换器和卷积神经网络，以捕捉长程依赖关系和局部特征，从而改进上下文表示。我们在一个有限的小鼠胫骨三维µCT扫描数据集上训练了DBAHNet，并在从七项不同研究中收集的多样化数据集上评估了其性能。该评估涵盖了分辨率、年龄、小鼠品系、药物治疗、手术程序和机械负荷的变化。DBAHNet表现出优异的性能，实现了高精度，尤其是在骨形态显著改变的具有挑战性的场景中。该模型的稳健性和泛化能力在各种不同的、未见过的条件下经过了严格测试，证实了其在自动分割高分辨率µCT小鼠胫骨扫描中的有效性。我们的研究结果突出了DBAHNet在提供可靠且准确的三维µCT小鼠胫骨分割方面的潜力，从而加强并加速药物开发中的临床前骨研究。该模型和代码可在https://github.com/bigfahma/DBAHNet获取。
