预测骨小梁立方体刚度张量的基于 QCT 深度转移学习的新方法

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-03-18 DOI:10.1016/j.irbm.2024.100831
Pengwei Xiao , Tinghe Zhang , Yufei Huang , Xiaodu Wang
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引用次数: 0

摘要

本研究旨在证明这样一个概念,即在生成模型的辅助下,迁移学习技术可用于减轻在预测骨小梁刚度张量时训练高保真深度学习(DL)模型所需的 "大数据 "要求。研究采用了领域适应的迁移学习方法,其中源领域包括由生成模型合成的 1,641 个数字骨小梁立方体,目标领域包括来自人类尸体股骨的 868 个真实骨小梁立方体。合成骨立方体和真实骨立方体的模拟定量计算机断层扫描(QCT)图像被用作输入,而这些立方体的刚度张量则通过有限元模拟确定作为输出。使用了三种迁移学习算法,包括基于实例(TrAdaBoostR2 和 WANN)和基于参数(RNN)的方法。为了评估这些深度迁移学习模型与使用目标领域数据集训练的基础深度学习(DL)模型的对比情况,进行了两项案例研究,一项是使用不同规模的训练数据集,另一项是使用有性别偏见的训练数据集。结果表明,这些深度迁移学习模型对样本大小和有性别偏见的训练数据集都很稳健,而基础 DL 模型对这些变化非常敏感。在三种迁移学习算法中,基于 RNN 的深度迁移学习模型的预测准确率最高(0.92%-0.96%),与使用目标领域数据集训练的基础 DL 模型的预测准确率相当。这项研究证明了所提出的概念,并证实基于 QCT 的高保真深度学习模型可用于预测骨小梁立方体的刚度张量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Novel QCT-Based Deep Transfer Learning Approach for Predicting Stiffness Tensor of Trabecular Bone Cubes

A Novel QCT-Based Deep Transfer Learning Approach for Predicting Stiffness Tensor of Trabecular Bone Cubes

Objectives

This study was performed to prove the concept that transfer learning techniques, assisted with a generative model, could be used to alleviate the ‘big data’ requirement for training high-fidelity deep learning (DL) models in prediction of stiffness tensor of trabecular bone cubes.

Material and methods

Transfer learning approaches of domain adaptation were used, in which a source domain included 1,641 digital trabecular bone cubes synthesized from a generative model, and a target domain included 868 real trabecular bone cubes from human cadaver femurs. Simulated quantitative computed tomography (QCT) images of both the synthesized and real bone cubes were used as input, whereas the stiffness tensor of these cubes determined using finite element simulations were used as output. Three transfer learning algorithms, including instance-based (TrAdaBoostR2 and WANN) and parameter-based (RNN) methods, were used. Two case studies, one with varying sizes of training dataset and the other with a gender-biased training dataset, were performed to evaluate these deep transfer learning models in comparison with a base deep learning (DL) model trained using the dataset from the target domain.

Results

The results indicated that these deep transfer learning models were robust both to sample size and to the gender-biased training dataset, whereas the base DL model was very sensitive to such changes. Among the three transfer learning algorithms, the prediction accuracy of the RNN-based deep transfer learning model was the best (0.92-0.96%) and comparable to that of the base DL model trained using the dataset from the target domain.

Conclusion

This study proved the proposed concept and confirmed that high fidelity QCT-based deep learning models could be obtained for prediction of stiffness tensor of trabecular bone cubes.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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