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Zhang C, Zhao M, Xie Y, Ding R, Ma M, Guo K, Jiang H, Xi W, Xia L. TL-MSE 2-Net: Transfer learning based nested model for cerebrovascular segmentation with aneurysms. Comput Biol Med 2023; 167:107609. [PMID: 37883854 DOI: 10.1016/j.compbiomed.2023.107609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
Cerebrovascular (i.e., cerebral vessel) segmentation is essential for diagnosing and treating brain diseases. Convolutional neural network models, such as U-Net, are commonly used for this purpose. Unfortunately, such models may not be entirely satisfactory in dealing with cerebrovascular segmentation with tumors due to the following issues: (1) Relatively small number of clinical datasets from patients obtained through different modalities such as computed tomography (CT) and magnetic resonance imaging (MRI), leading to inadequate training and lack of transferability in the modeling; (2) Insufficient feature extraction caused by less attention to both convolution sizes and cerebral vessel edges. Inspired by the existence of similar features on cerebral vessels between normal subjects and patients, we propose a transfer learning strategy based on a pre-trained nested model called TL-MSE2-Net. This model uses one of the publicly available datasets for cerebrovascular segmentation with aneurysms. To address issue (1), our transfer learning strategy leverages a pre-trained model that uses a large number of datasets from normal subjects, providing a potential solution to the lack of sufficient clinical datasets. To tackle issue (2), we structure the pre-trained model based on 3D U-Net, comprising three blocks: ResMul, DeRes, and REAM. The ResMul and DeRes blocks enhance feature extraction by utilizing multiple convolution sizes to capture multiscale features, and the REAM block increases the weight of the voxels on the edges of the given 3D volume. We evaluated the proposed model on one small private clinical dataset and two publicly available datasets. The experimental results demonstrated that our MSE2-Net framework achieved an average Dice score of 70.81 % and 89.08 % on the two publicly available datasets, outperforming other state-of-the-art methods. Ablation studies were also conducted to validate the effectiveness of each block. The proposed TL-MSE2-Net yielded better results than MSE2-Net on a small private clinical dataset, with increases of 5.52 %, 3.37 %, 6.71 %, and 0.85 % for the Dice score, sensitivity, Jaccard index, and precision, respectively.
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Affiliation(s)
- Chaoran Zhang
- Laboratory of Neural Computing and Intelligent Perception (NCIP), Capital Normal University, Beijing, 100048, China
| | - Ming Zhao
- Department of Neurosurgery, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yixuan Xie
- Laboratory of Neural Computing and Intelligent Perception (NCIP), Capital Normal University, Beijing, 100048, China
| | - Rui Ding
- Laboratory of Neural Computing and Intelligent Perception (NCIP), Capital Normal University, Beijing, 100048, China
| | - Ming Ma
- Department of Computer Science, Winona State University, Winona, MN, 55987, USA
| | - Kaiwen Guo
- Laboratory of Neural Computing and Intelligent Perception (NCIP), Capital Normal University, Beijing, 100048, China
| | - Hongzhen Jiang
- Department of Neurosurgery, First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Wei Xi
- Department of Radiology, Fourth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China
| | - Likun Xia
- Laboratory of Neural Computing and Intelligent Perception (NCIP), Capital Normal University, Beijing, 100048, China.
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Lee T, Wollstein G, Madu CT, Wronka A, Zheng L, Zambrano R, Schuman JS, Hu J. Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality. Transl Vis Sci Technol 2023; 12:2. [PMID: 38038606 PMCID: PMC10697175 DOI: 10.1167/tvst.12.12.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Purpose Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. Method We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. Results Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. Conclusions The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. Translational Relevance TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.
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Affiliation(s)
- TingFang Lee
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Departments of Population Health, NYU Langone Health, New York, NY, USA
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
- Center of Neural Science, NYU College of Arts and Sciences, New York, NY, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA
| | - Chisom T Madu
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Andrew Wronka
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Lei Zheng
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Ronald Zambrano
- Department of Ophthalmology, NYU Langone Health, New York, NY, USA
| | - Joel S Schuman
- Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA
| | - Jiyuan Hu
- Departments of Population Health, NYU Langone Health, New York, NY, USA
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Lu Z, Wang T, Zhang R. Editorial: Affective brain-computer interface in emotion artificial intelligence and medical engineering. Front Comput Neurosci 2023; 17:1237252. [PMID: 37496515 PMCID: PMC10367346 DOI: 10.3389/fncom.2023.1237252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023] Open
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Fei H, Wang Q, Shang F, Xu W, Chen X, Chen Y, Li H. HC-Net: A hybrid convolutional network for non-human primate brain extraction. Front Comput Neurosci 2023; 17:1113381. [PMID: 36846727 PMCID: PMC9947775 DOI: 10.3389/fncom.2023.1113381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks.
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Affiliation(s)
- Hong Fei
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Qianshan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Fangxin Shang
- Country Intelligent Healthcare Unit, Baidu, Beijing, China
| | - Wenyi Xu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xiaofeng Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yifei Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Haifang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China,*Correspondence: Haifang Li,
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