论混合深度三维卷积神经网络算法在预测脑白质微观力学中的应用

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-08-22 DOI:10.1016/j.cmpb.2024.108381
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引用次数: 0

摘要

背景:由于三维微观结构固有的各向异性以及异质脑组织(轴突、髓鞘和胶质细胞)之间的各种相互作用,脑白质(BWM)的材料表征非常困难。然而,开发能准确表示微观和宏观脑组织之间关系的全尺寸有限元模型极具挑战性,且计算成本高昂。在频域粘弹性条件下,通过构建单元格计算出的 BWM 微观结构的各向异性属性包括损耗模量和存储模量各 36 个独立常数。此外,在一个无限数据集中,每个单元格的结构都是任意的。方法:在本研究中,我们扩展了之前在频域中开发 BWM 微观结构代表性体积元素(RVE)的工作,开发出了可预测各向异性复合材料特性的三维深度学习算法。深度三维卷积神经网络(CNN)算法利用体素化方法从三维 RVE 中获取几何信息。体素化位置中编码的结构信息被用作输入数据,同时交叉引用 RVE 的材料属性(输出数据)。结果:本文介绍了预测 BWM 各向异性复合材料属性的不同 CNN 算法。意义:与基线 CNN 算法相比,所提出的多尺度 3D ResNet(M3DR)算法在预测 BWM 组织属性方面表现出较高的学习能力和性能。混合 M3DR 框架还克服了仅使用有限元对脑组织建模时遇到的重大限制,包括计算成本高、网格和模拟失败等问题。所提出的框架还提供了一个高效、精简的平台,可用于实施复杂的边界条件、建立内在材料属性模型和传递界面结构信息。
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On the application of hybrid deep 3D convolutional neural network algorithms for predicting the micromechanics of brain white matter

Background:

Material characterization of brain white matter (BWM) is difficult due to the anisotropy inherent to the three-dimensional microstructure and the various interactions between heterogeneous brain-tissue (axon, myelin, and glia). Developing full scale finite element models that accurately represent the relationship between the micro and macroscale BWM is however extremely challenging and computationally expensive. The anisotropic properties of the microstructure of BWM computed by building unit cells under frequency domain viscoelasticity comprises of 36 individual constants each, for the loss and storage moduli. Furthermore, the architecture of each unit cell is arbitrary in an infinite dataset.

Methods:

In this study, we extend our previous work on developing representative volume elements (RVE) of the microstructure of the BWM in the frequency domain to develop 3D deep learning algorithms that can predict the anisotropic composite properties. The deep 3D convolutional neural network (CNN) algorithms utilizes a voxelization method to obtain geometry information from 3D RVEs. The architecture information encoded in the voxelized location is employed as input data while cross-referencing the RVEs’ material properties (output data). We further develop methods by incorporating parallel pathways, Residual Neural Networks and inception modulus that improve the efficiency of deep learning algorithms.

Results:

This paper presents different CNN algorithms in predicting the anisotropic composite properties of BWM. A quantitative analysis of the individual algorithms is presented with the view of identifying optimal strategies to interpret the combined measurements of brain MRE and DTI.

Significance:

The proposed Multiscale 3D ResNet (M3DR) algorithm demonstrates high learning ability and performance over baseline CNN algorithms in predicting BWM tissue properties. The hybrid M3DR framework also overcomes the significant limitations encountered in modeling brain tissue using finite elements alone including those such as high computational cost, mesh and simulation failure. The proposed framework also provides an efficient and streamlined platform for implementing complex boundary conditions, modeling intrinsic material properties and imparting interfacial architecture information.

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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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