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Kim SE, Nam JW, Kim JI, Kim JK, Ro DH. Enhanced deep learning model enables accurate alignment measurement across diverse institutional imaging protocols. Knee Surg Relat Res 2024; 36:4. [PMID: 38217058 PMCID: PMC10785531 DOI: 10.1186/s43019-023-00209-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 12/27/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND Achieving consistent accuracy in radiographic measurements across different equipment and protocols is challenging. This study evaluates an advanced deep learning (DL) model, building upon a precursor, for its proficiency in generating uniform and precise alignment measurements in full-leg radiographs irrespective of institutional imaging differences. METHODS The enhanced DL model was trained on over 10,000 radiographs. Utilizing a segmented approach, it separately identified and evaluated regions of interest (ROIs) for the hip, knee, and ankle, subsequently integrating these regions. For external validation, 300 datasets from three distinct institutes with varied imaging protocols and equipment were employed. The study measured seven radiologic parameters: hip-knee-ankle angle, lateral distal femoral angle, medial proximal tibial angle, joint line convergence angle, weight-bearing line ratio, joint line obliquity angle, and lateral distal tibial angle. Measurements by the model were compared with an orthopedic specialist's evaluations using inter-observer and intra-observer intraclass correlation coefficients (ICCs). Additionally, the absolute error percentage in alignment measurements was assessed, and the processing duration for radiograph evaluation was recorded. RESULTS The DL model exhibited excellent performance, achieving an inter-observer ICC between 0.936 and 0.997, on par with an orthopedic specialist, and an intra-observer ICC of 1.000. The model's consistency was robust across different institutional imaging protocols. Its accuracy was particularly notable in measuring the hip-knee-ankle angle, with no instances of absolute error exceeding 1.5 degrees. The enhanced model significantly improved processing speed, reducing the time by 30-fold from an initial 10-11 s to 300 ms. CONCLUSIONS The enhanced DL model demonstrated its ability for accurate, rapid alignment measurements in full-leg radiographs, regardless of protocol variations, signifying its potential for broad clinical and research applicability.
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Affiliation(s)
- Sung Eun Kim
- Department of Orthopaedic Surgery, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
- Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, South Korea
| | | | - Joong Il Kim
- Department of Orthopaedic Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, South Korea
| | - Jong-Keun Kim
- Department of Orthopaedic Surgery, Heung-K Hospital, Gyeonggi-do, South Korea
| | - Du Hyun Ro
- Department of Orthopaedic Surgery, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea.
- Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, South Korea.
- CONNECTEVE Co., Ltd, Seoul, South Korea.
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Yuan C, Song S, Yang J, Sun Y, Yang B, Xu L. Pulmonary arteries segmentation from CT images using PA-Net with attention module and contour loss. Med Phys 2023; 50:4887-4898. [PMID: 36752170 DOI: 10.1002/mp.16265] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 01/03/2023] [Accepted: 01/18/2023] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Pulmonary embolism is a kind of cardiovascular disease that threatens human life and health. Since pulmonary embolism exists in the pulmonary artery, improving the segmentation accuracy of pulmonary artery is the key to the diagnosis of pulmonary embolism. Traditional medical image segmentation methods have limited effectiveness in pulmonary artery segmentation. In recent years, deep learning methods have been gradually adopted to solve complex problems in the field of medical image segmentation. PURPOSE Due to the irregular shape of the pulmonary artery and the adjacent-complex tissues, the accuracy of the existing pulmonary artery segmentation methods based on deep learning needs to be improved. Therefore, the purpose of this paper is to develop a segmentation network, which can obtain higher segmentation accuracy and further improve the diagnosis effect. METHODS In this study, the pulmonary artery segmentation performance from the network model and loss function is improved, proposing a pulmonary artery segmentation network (PA-Net) to segment the pulmonary artery region from 2D CT images. Reverse Attention and edge attention are used to enhance the expression ability of the boundary. In addition, to better use feature information, the channel attention module is introduced in the decoder to highlight the important channel features and suppress the unimportant channels. Due to blurred boundaries, pixels near the boundaries of the pulmonary artery may be difficult to segment. Therefore, a new contour loss function based on the active contour model is proposed in this study to segment the target region by assigning dynamic weights to false positive and false negative regions and accurately predict the boundary structure. RESULTS The experimental results show that the segmentation accuracy of this proposed method is significantly improved in comparison with state-of-the-art segmentation methods, and the Dice coefficient is 0.938 ± 0.035, which is also confirmed from the 3D reconstruction results. CONCLUSIONS Our proposed method can accurately segment pulmonary artery structure. This new development will provide the possibility for further rapid diagnosis of pulmonary artery diseases such as pulmonary embolism. Code is available at https://github.com/Yuanyan19/PA-Net.
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Affiliation(s)
- Chengyan Yuan
- School of Science, Northeastern University, Shenyang, China
| | - Shuni Song
- School of Data and Computer Science, Guangdong Peizheng College, Guangzhou, China
| | - Jinzhong Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yu Sun
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Benqiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Lisheng Xu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Medical Image Computing, Ministry of Education, Shenyang, Liaoning, China
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, Liaoning, China
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Yuan L, Song J, Fan Y. FM-Unet: Biomedical image segmentation based on feedback mechanism Unet. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:12039-12055. [PMID: 37501431 DOI: 10.3934/mbe.2023535] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
With the development of deep learning, medical image segmentation technology has made significant progress in the field of computer vision. The Unet is a pioneering work, and many researchers have conducted further research based on this architecture. However, we found that most of these architectures are improvements in the backward propagation and integration of the network, and few changes are made to the forward propagation and information integration of the network. Therefore, we propose a feedback mechanism Unet (FM-Unet) model, which adds feedback paths to the encoder and decoder paths of the network, respectively, to help the network fuse the information of the next step in the current encoder and decoder. The problem of encoder information loss and decoder information shortage can be well solved. The proposed model has more moderate network parameters, and the simultaneous multi-node information fusion can alleviate the gradient disappearance. We have conducted experiments on two public datasets, and the results show that FM-Unet achieves satisfactory results.
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Affiliation(s)
- Lei Yuan
- The Key Laboratory of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou 363000, China
| | - Jianhua Song
- The Key Laboratory of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou 363000, China
- College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
| | - Yazhuo Fan
- College of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
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Du X, Chen Z, Li Q, Yang S, Jiang L, Yang Y, Li Y, Gu Z. Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence. Biodes Manuf 2023; 6:319-339. [PMID: 36713614 PMCID: PMC9867835 DOI: 10.1007/s42242-022-00226-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/06/2022] [Indexed: 01/21/2023]
Abstract
In modern terminology, "organoids" refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions. Graphic abstract
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Affiliation(s)
- Xuan Du
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096 China
| | - Zaozao Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096 China
| | - Qiwei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096 China
| | - Sheng Yang
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009 China
| | - Lincao Jiang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096 China
| | - Yi Yang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096 China
| | - Yanhui Li
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210008 China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096 China
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Karri M, Annavarapu CSR, Acharya UR. Explainable multi-module semantic guided attention based network for medical image segmentation. Comput Biol Med 2022; 151:106231. [PMID: 36335811 DOI: 10.1016/j.compbiomed.2022.106231] [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: 08/23/2022] [Revised: 10/05/2022] [Accepted: 10/11/2022] [Indexed: 12/27/2022]
Abstract
Automated segmentation of medical images is crucial for disease diagnosis and treatment planning. Medical image segmentation has been improved based on the convolutional neural networks (CNNs) models. Unfortunately, they are still limited by scenarios in which the segmentation objective has large variations in size, boundary, position, and shape. Moreover, current CNNs have low explainability, restricting their use in clinical decisions. In this paper, we involve substantial use of various attentions in a CNN model and present an explainable multi-module semantic guided attention based network (MSGA-Net) for explainable and highly accurate medical image segmentation, which involves considering the most significant spatial regions, boundaries, scales, and channels. Specifically, we present a multi-scale attention module (MSA) to extract the most salient features at various scales from medical images. Then, we propose a semantic region-guided attention mechanism (SRGA) including location attention (LAM), channel-wise attention (CWA), and edge attention (EA) modules to extract the most important spatial, channel-wise, boundary-related features for interested regions. Moreover, we present a sequence of fine-tuning steps with the SRGA module to gradually weight the significance of interesting regions while simultaneously reducing the noise. In this work, we experimented with three different types of medical images such as dermoscopic images (HAM10000 dataset), multi-organ CT images (CHAOS 2019 dataset), and Brain tumor MRI images (BraTS 2020 dataset). Extensive experiments on all types of medical images revealed that our proposed MSGA-Net substantially increased the overall performance of all metrics over the existing models. Moreover, displaying the attention feature maps has more explainability than state-of-the-art models.
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Affiliation(s)
- Meghana Karri
- Computer Science and Engineering Department, Indian Institute of Technology (ISM), Dhanbad, 826004, Jharkhand, India.
| | | | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of science and Technology, SUSS university, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia university, Taichung, Taiwan.
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Abbas F, Malah M, Babahenini MC. Approximating global illumination with ambient occlusion and environment light via generative adversarial networks. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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7
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VMAT dose prediction in radiotherapy by using progressive refinement UNet. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Lu F, Fu C, Zhang G, Shi J. Adaptive multi-scale feature fusion based U-net for fracture segmentation in coal rock images. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-211968] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Accurate segmentation of fractures in coal rock CT images is important for the development of coalbed methane. However, due to the large variation of fracture scale and the similarity of gray values between weak fractures and the surrounding matrix, it remains a challenging task. And there is no published dataset of coal rock, which make the task even harder. In this paper, a novel adaptive multi-scale feature fusion method based on U-net (AMSFF-U-net) is proposed for fracture segmentation in coal rock CT images. Specifically, encoder and decoder path consist of residual blocks (ReBlock), respectively. The attention skip concatenation (ASC) module is proposed to capture more representative and distinguishing features by combining the high-level and low-level features of adjacent layers. The adaptive multi-scale feature fusion (AMSFF) module is presented to adaptively fuse different scale feature maps of encoder path; it can effectively capture rich multi-scale features. In response to the lack of coal rock fractures training data, we applied a set of comprehensive data augmentation operations to increase the diversity of training samples. These extensive experiments are conducted via seven state-of-the-art methods (i.e., FCEM, U-net, Res-Unet, Unet++, MSN-Net, WRAU-Net and ours). The experiment results demonstrate that the proposed AMSFF-U-net can achieve better segmentation performance in our works, particularly for weak fractures and tiny scale fractures.
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Affiliation(s)
- Fengli Lu
- School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
| | - Chengcai Fu
- School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
| | - Guoying Zhang
- School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
| | - Jie Shi
- School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, China
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Guo X, Chen Z, Liu J, Yuan Y. Non-equivalent images and pixels: confidence-aware resampling with meta-learning mixup for polyp segmentation. Med Image Anal 2022; 78:102394. [DOI: 10.1016/j.media.2022.102394] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 12/07/2021] [Accepted: 02/11/2022] [Indexed: 01/27/2023]
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An unsupervised approach of colonic polyp segmentation using adaptive markov random fields. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2021.12.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Lin D, Li Y, Prasad S, Nwe TL, Dong S, Oo ZM. CAM-guided Multi-Path Decoding U-Net with Triplet Feature Regularization for Defect Detection and Segmentation. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107272] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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12
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Brady L, Wang YN, Rombokas E, Ledoux WR. Comparison of texture-based classification and deep learning for plantar soft tissue histology segmentation. Comput Biol Med 2021; 134:104491. [PMID: 34090017 PMCID: PMC8263502 DOI: 10.1016/j.compbiomed.2021.104491] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 05/10/2021] [Accepted: 05/10/2021] [Indexed: 11/22/2022]
Abstract
Histomorphological measurements can be used to identify microstructural changes related to disease pathomechanics, in particular, plantar soft tissue changes with diabetes. However, these measurements are time-consuming and susceptible to sampling and human measurement error. We investigated two approaches to automate segmentation of plantar soft tissue stained with modified Hart's stain for elastin with the eventual goal of subsequent morphological analysis. The first approach used multiple texture- and color-based features with tile-wise classification. The second approach used a convolutional neural network modified from the U-Net architecture with fewer channel dimensions and additional downsampling steps. A hybrid color and texture feature, Fourier reduced histogram of uniform improved opponent color local binary patterns (f-IOCLBP), yielded the best feature-based segmentation, but still performed 3.6% worse on average than the modified U-Net. The texture-based method was sensitive to changes in illumination and stain intensity, and segmentation errors were often in large regions of single tissues or at tissue boundaries. The U-Net was able to segment small, few-pixel tissue boundaries, and errors were often trivial to clean up with post-processing. A U-Net approach outperforms hand-crafted features for segmentation of plantar soft tissue stained with modified Hart's stain for elastin.
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Affiliation(s)
- Lynda Brady
- Center for Limb Loss and MoBility (CLiMB), VA Puget Sound, Seattle, WA, 98108, USA; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA
| | - Yak-Nam Wang
- Center for Limb Loss and MoBility (CLiMB), VA Puget Sound, Seattle, WA, 98108, USA; Center for Industrial and Medical Ultrasound, Applied Physics Laboratory, University of Washington, Seattle, WA, 98195, USA
| | - Eric Rombokas
- Center for Limb Loss and MoBility (CLiMB), VA Puget Sound, Seattle, WA, 98108, USA; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA; Department of Electrical Engineering, University of Washington, Seattle, WA, 98195, USA
| | - William R Ledoux
- Center for Limb Loss and MoBility (CLiMB), VA Puget Sound, Seattle, WA, 98108, USA; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA; Department of Orthopaedics and Sports Medicine, University of Washington, Seattle, WA, 98195, USA.
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Yıldız E, Arslan AT, Yıldız Taş A, Acer AF, Demir S, Şahin A, Erol Barkana D. Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images. Transl Vis Sci Technol 2021; 10:33. [PMID: 34038501 PMCID: PMC8161698 DOI: 10.1167/tvst.10.6.33] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 05/05/2021] [Indexed: 11/24/2022] Open
Abstract
Purpose In vivo confocal microscopy (IVCM) is a noninvasive, reproducible, and inexpensive diagnostic tool for corneal diseases. However, widespread and effortless image acquisition in IVCM creates serious image analysis workloads on ophthalmologists, and neural networks could solve this problem quickly. We have produced a novel deep learning algorithm based on generative adversarial networks (GANs), and we compare its accuracy for automatic segmentation of subbasal nerves in IVCM images with a fully convolutional neural network (U-Net) based method. Methods We have collected IVCM images from 85 subjects. U-Net and GAN-based image segmentation methods were trained and tested under the supervision of three clinicians for the segmentation of corneal subbasal nerves. Nerve segmentation results for GAN and U-Net-based methods were compared with the clinicians by using Pearson's R correlation, Bland-Altman analysis, and receiver operating characteristics (ROC) statistics. Additionally, different noises were applied on IVCM images to evaluate the performances of the algorithms with noises of biomedical imaging. Results The GAN-based algorithm demonstrated similar correlation and Bland-Altman analysis results with U-Net. The GAN-based method showed significantly higher accuracy compared to U-Net in ROC curves. Additionally, the performance of the U-Net deteriorated significantly with different noises, especially in speckle noise, compared to GAN. Conclusions This study is the first application of GAN-based algorithms on IVCM images. The GAN-based algorithms demonstrated higher accuracy than U-Net for automatic corneal nerve segmentation in IVCM images, in patient-acquired images and noise applied images. This GAN-based segmentation method can be used as a facilitating diagnostic tool in ophthalmology clinics. Translational Relevance Generative adversarial networks are emerging deep learning models for medical image processing, which could be important clinical tools for rapid segmentation and analysis of corneal subbasal nerves in IVCM images.
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Affiliation(s)
- Erdost Yıldız
- Koç University Research Center for Translational Medicine, Koç University, Istanbul, Turkey
| | | | - Ayşe Yıldız Taş
- Department of Ophthalmology, Koç University School of Medicine, Istanbul, Turkey
| | | | - Sertaç Demir
- Techy Bilişim Ltd., Eskişehir, Turkey
- Department of Computer Engineering, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Afsun Şahin
- Koç University Research Center for Translational Medicine, Koç University, Istanbul, Turkey
- Department of Ophthalmology, Koç University School of Medicine, Istanbul, Turkey
| | - Duygun Erol Barkana
- Department of Electrical and Electronics Engineering, Yeditepe University, Istanbul, Turkey
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Gao C, Ye H, Cao F, Wen C, Zhang Q, Zhang F. Multiscale fused network with additive channel–spatial attention for image segmentation. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106754] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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