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Rezaeitaleshmahalleh M, Lyu Z, Mu N, Jiang J. USING CONVOLUTIONAL NEURAL NETWORK-BASED SEGMENTATION FOR IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF BRAIN ANEURYSMS: INITIAL EXPERIENCE IN AUTOMATED MODEL CREATION. J MECH MED BIOL 2023; 23:2340055. [PMID: 38523806 PMCID: PMC10956116 DOI: 10.1142/s0219519423400559] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
"Image-based" computational fluid dynamics (CFD) simulations provide insights into each patient's hemodynamic environment. However, current standard procedures for creating CFD models start with manual segmentation and are time-consuming, hindering the clinical translation of image-based CFD simulations. This feasibility study adopts deep-learning-based image segmentation (hereafter referred to as Artificial Intelligence (AI) segmentation) to replace manual segmentation to accelerate CFD model creation. Two published convolutional neural network-based AI methods (MIScnn and DeepMedic) were selected to perform CFD model extraction from three-dimensional (3D) rotational angiography data containing intracranial aneurysms. In this study, aneurysm morphological and hemodynamic results using models generated by AI segmentation methods were compared with those obtained by two human users for the same data. Interclass coefficients (ICC), Bland-Altman plots, and Pearson's correlation coefficients (PCC) were combined to assess how well AI-generated CFD models were performed. We found that almost perfect agreement was obtained between the human and AI results for all eleven morphological and five out of eight hemodynamic parameters, while a moderate agreement was obtained from the remaining three hemodynamic parameters. Given this level of agreement, using AI segmentation to create CFD models is feasible, given more developments.
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Affiliation(s)
- Mostafa Rezaeitaleshmahalleh
- Dept. of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive Houghton, Michigan 49931, USA
| | - Zonghan Lyu
- Dept. of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive Houghton, Michigan 49931, USA
| | - Nan Mu
- Dept. of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive Houghton, Michigan 49931, USA
| | - Jingfeng Jiang
- Depts. of Biomedical Engineering, Mechanical Engineering and Engineering Mechanics, and Computer Science, Michigan Technological University, 1400 Townsend Drive Houghton, Michigan 49931, USA
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Lima EABF, Oden JT, Hormuth DA, Yankeelov TE, Almeida RC. Selection, calibration, and validation of models of tumor growth. Math Models Methods Appl Sci 2016; 26:2341-2368. [PMID: 28827890 PMCID: PMC5560997 DOI: 10.1142/s021820251650055x] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology (in vivo). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.
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Affiliation(s)
- E. A. B. F. Lima
- Center of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX 78712-1229, USA
| | - J. T. Oden
- Center of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX 78712-1229, USA
- Corresponding author:
| | - D. A. Hormuth
- Center of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX 78712-1229, USA
| | - T. E. Yankeelov
- Center of Computational Oncology, Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th St, Austin, TX 78712-1229, USA
- Department of Internal Medicine, Livestrong Cancer Institutes, Dell Medical School, Health Learning Building, The University of Texas at Austin, 1501 Red River Street, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton, Austin, TX 78712, USA
| | - R. C. Almeida
- National Laboratory for Scientific Computing (LNCC), Av. Getúlio Vargas, 333, Quitandinha, Petrópolis, Rio de Janeiro 25651-075, Brazil
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