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Ashok M, Gupta A. Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3 Model. J Comput Biol 2023; 30:346-362. [PMID: 36629856 DOI: 10.1089/cmb.2022.0248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
The objective of this article is to automatically segment organs at risk (OARs) for thoracic radiology in computed tomography (CT) scan images. The OARs in the thoracic anatomical region during the radiotherapy treatment are mainly the neighbouring organs such as the esophagus, heart, trachea, and aorta. The dataset of 40 patients was used in the proposed work by splitting it into three parts: training, validation, and test sets. The implementation was performed on the Google Colab Pro+ framework with 52 GB of RAM and 265 GB of storage space. An ensemble model was evolved for the automatic segmentation of four OARs in thoracic CT images. U-Net with InceptionV3 as the backbone was used, and different hyperparameters were used during the training of the model. The proposed model achieved precise accuracy for OARs segmentation with an average dice coefficient of 0.9413, Hausdorff value of 0.1838, sensitivity of 0.9783, and specificity of 0.9895 on the Test dataset. An ensembled U-Net InceptionV3 model has been proposed, improving the segmentation results compared with the state-of-the-art techniques such as U-Net, ResNet, Vgg16, etc. The results of the experiments revealed that the proposed model effectively improved the performance of the segmentation of the esophagus, heart, trachea, and aorta.
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Affiliation(s)
- Malvika Ashok
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Abhishek Gupta
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
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Shoaib MA, Chuah JH, Ali R, Hasikin K, Khalil A, Hum YC, Tee YK, Dhanalakshmi S, Lai KW. An Overview of Deep Learning Methods for Left Ventricle Segmentation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:4208231. [PMID: 36756163 PMCID: PMC9902166 DOI: 10.1155/2023/4208231] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/25/2022] [Accepted: 11/24/2022] [Indexed: 01/31/2023]
Abstract
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.
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Affiliation(s)
- Muhammad Ali Shoaib
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Raza Ali
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azira Khalil
- Faculty of Science & Technology, Universiti Sains Islam Malaysia, Nilai 71800, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India
| | - Khin Wee Lai
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
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Hum YC, Tan HR, Tee YK, Yap WS, Tan TS, Salim MIM, Lai KW. The development of skin lesion detection application in smart handheld devices using deep neural networks. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:41579-41610. [DOI: 10.1007/s11042-021-11013-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 04/29/2021] [Accepted: 05/05/2021] [Indexed: 07/26/2024]
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Kang BK, Han Y, Oh J, Lim J, Ryu J, Yoon MS, Lee J, Ryu S. Automatic Segmentation for Favourable Delineation of Ten Wrist Bones on Wrist Radiographs Using Convolutional Neural Network. J Pers Med 2022; 12:776. [PMID: 35629198 PMCID: PMC9147335 DOI: 10.3390/jpm12050776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/05/2022] [Accepted: 05/10/2022] [Indexed: 02/04/2023] Open
Abstract
Purpose: This study aimed to develop and validate an automatic segmentation algorithm for the boundary delineation of ten wrist bones, consisting of eight carpal and two distal forearm bones, using a convolutional neural network (CNN). Methods: We performed a retrospective study using adult wrist radiographs. We labeled the ground truth masking of wrist bones, and propose that the Fine Mask R-CNN consisted of wrist regions of interest (ROI) using a Single-Shot Multibox Detector (SSD) and segmentation via Mask R-CNN, plus the extended mask head. The primary outcome was an improvement in the prediction of delineation via the network combined with ground truth masking, and this was compared between two networks through five-fold validations. Results: In total, 702 images were labeled for the segmentation of ten wrist bones. The overall performance (mean (SD] of Dice coefficient) of the auto-segmentation of the ten wrist bones improved from 0.93 (0.01) using Mask R-CNN to 0.95 (0.01) using Fine Mask R-CNN (p < 0.001). The values of each wrist bone were higher when using the Fine Mask R-CNN than when using the alternative (all p < 0.001). The value derived for the distal radius was the highest, and that for the trapezoid was the lowest in both networks. Conclusion: Our proposed Fine Mask R-CNN model achieved good performance in the automatic segmentation of ten overlapping wrist bones derived from adult wrist radiographs.
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Affiliation(s)
- Bo-kyeong Kang
- Department of Radiology, College of Medicine, Hanyang University, Seoul 04763, Korea;
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04764, Korea; (M.S.Y.); (J.L.)
| | - Yelin Han
- Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Jaehoon Oh
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04764, Korea; (M.S.Y.); (J.L.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea
| | - Jongwoo Lim
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04764, Korea; (M.S.Y.); (J.L.)
- Department of Computer Science, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea;
| | - Jongbin Ryu
- Department of Software and Computer Engineering, Ajou University, Suwon 16499, Korea;
- Department of Artificial Intelligence, Ajou University, Suwon 16499, Korea
| | - Myeong Seong Yoon
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04764, Korea; (M.S.Y.); (J.L.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea
| | - Juncheol Lee
- Machine Learning Research Center for Medical Data, Hanyang University, Seoul 04764, Korea; (M.S.Y.); (J.L.)
- Department of Emergency Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea
| | - Soorack Ryu
- Biostatistical Consulting and Research Lab, Medical Research Collaborating Center, Hanyang University, Seoul 04763, Korea;
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Nakatsu K, Rahman R, Morita K, Fujita D, Kobashi S. Automatic Carpal Site Detection Method for Evaluation of Rheumatoid Arthritis Using Deep Learning. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2022. [DOI: 10.20965/jaciii.2022.p0042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Approximately 600,000 to 1,000,000 patients are diagnosed with rheumatoid arthritis (RA) in Japan. To provide appropriate treatment, it is necessary to accurately measure the progression of RA by diagnosing the disease several times a year. The modified total sharp score (mTSS) calculated from hand X-ray images is a standard diagnostic method for RA progression. However, this diagnostic method is time-consuming as the scores are rated at as many as 16 points per hand. Accordingly, in order to shorten the diagnosis time of RA patients and improve the quality of diagnosis, the development of computer-aided diagnosis (CAD) systems is expected. We have previously proposed a CAD system that can detect finger joint positions using a support vector machine and can estimate the mTSS using ridge regression. In this study, we propose a fully automatic detection method of RA score evaluation points in the carpal site from simple hand X-ray images using deep learning. The proposed method first segments the carpal site using deep learning. Next, the RA evaluation points are automatically determined from each segment based on prior knowledge. Experimental results on X-ray images of the hands of 140 patients with RA showed that the mTSS evaluation point at the carpal site could be detected with an average error of 25 pixels. This study enables the automatic detection of RA score evaluation points in the carpal site. In the diagnosis of RA, the time required for diagnosis can be reduced by automating the determination of diagnostic points by physician.
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Alameri M, Hasikin K, Kadri NA, Nasir NFM, Mohandas P, Anni JS, Azizan MM. Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6953593. [PMID: 34497665 PMCID: PMC8421170 DOI: 10.1155/2021/6953593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 06/24/2021] [Accepted: 08/12/2021] [Indexed: 11/17/2022]
Abstract
Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.
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Affiliation(s)
- Mohammed Alameri
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia
| | - Nashrul Fazli Mohd Nasir
- Biomedical Electronic Engineering Program, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
- Sport Engineering Research Centre (SERC), Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
| | - Prabu Mohandas
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kerala, India
| | - Jerline Sheeba Anni
- Department of Computer Science and Engineering, MEA Engineering College, Kerala, India
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia
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Construction and Simulation of Financial Audit Model Based on Convolutional Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:1182557. [PMID: 34306046 PMCID: PMC8266457 DOI: 10.1155/2021/1182557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 06/23/2021] [Indexed: 11/17/2022]
Abstract
Big data has brought a new round of information revolution. Faced with the goal of full coverage of audit and supervision, making full use of big data is the main method to promote the realization of the goal of full coverage of audit and supervision. Data analysis and utilization is an indispensable task of auditing. Actively exploring multidimensional and intelligent data analysis methods and developing big data audit cases are the new development direction of auditing. The convolutional neural network's excellent ability to extract data features well meets the relevant requirements of financial auditing. However, in practical applications, convolutional neural networks often encounter various problems such as disappearance of gradients and difficulty in convergence, which reduces its expected performance in financial audit applications. In order to make the performance of the financial audit model based on convolutional neural network more excellent, after summarizing the characteristics of genetic algorithm, this article applies genetic algorithm to the optimization of the convolutional neural network model. We applied genetic algorithm to optimize the initial weights of the convolutional neural network. The error sensitivity and learning rate changes of different hidden layers are discussed, the influence of different learning rates on the convergence speed of convolutional neural networks is analyzed, and the recognition performance of other algorithms on financial audit data sets is simulated and compared. We conducted experiments on the network structure and parameter optimization on the financial audit database. The results show that the recognition error rate of the convolutional neural network model with improved learning rate algorithm in the financial audit data set is lower than that of the multilayer feedforward network, so it has better performance.
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