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Wu Y, Yang X, Wang M, Lian Y, Hou P, Chai X, Dai Q, Qian B, Jiang Y, Gao J. Artificial intelligence assisted automatic screening of opportunistic osteoporosis in computed tomography images from different scanners. Eur Radiol 2024:10.1007/s00330-024-11046-2. [PMID: 39231830 DOI: 10.1007/s00330-024-11046-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 06/09/2024] [Accepted: 08/08/2024] [Indexed: 09/06/2024]
Abstract
OBJECTIVES It is feasible to evaluate bone mineral density (BMD) and detect osteoporosis through an artificial intelligence (AI)-assisted system by using quantitative computed tomography (QCT) as a reference without additional radiation exposure or cost. METHODS A deep-learning model developed based on 3312 low-dose chest computed tomography (LDCT) scans (trained with 2337 and tested with 975) achieved a mean dice similarity coefficient of 95.8% for T1-T12, L1, and L2 vertebral body (VB) segmentation on test data. We performed a model evaluation based on 4401 LDCT scans (obtained from scanners of 3 different manufacturers as external validation data). The BMD values of all individuals were extracted from three consecutive VBs: T12 to L2. Line regression and Bland‒Altman analyses were used to evaluate the overall detection performance. Sensitivity and specificity were used to evaluate the diagnostic performance for normal, osteopenia, and osteoporosis patients. RESULTS Compared with the QCT results as the diagnostic standard, the BMD assessed had a mean error of (- 0.28, 2.37) mg/cm3. Overall, the sensitivity of a normal diagnosis was greater than that of a diagnosis of osteopenia or osteoporosis. For the diagnosis of osteoporosis, the model achieved a sensitivity > 86% and a specificity > 98%. CONCLUSION The developed tool is clinically applicable and helpful for the positioning and analysis of VBs, the measurement of BMD, and the screening of osteopenia and osteoporosis. CLINICAL RELEVANCE STATEMENT The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest CT scans and performed well on CT images collected from different scanners. KEY POINTS Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fractures. This system could automatically and opportunistically screen for osteoporosis using low-dose chest CT scans obtained for lung cancer screening. The developed system performed well on CT images collected from different scanners and did not differ with patient age or sex.
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Affiliation(s)
- Yan Wu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaopeng Yang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mingyue Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanbang Lian
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ping Hou
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiangfei Chai
- Department of Scientific Research, Huiying Medical Technology, Beijing, China
| | - Qiong Dai
- Department of Scientific Research, Huiying Medical Technology, Beijing, China
| | - Baoxin Qian
- Department of Scientific Research, Huiying Medical Technology, Beijing, China
| | - Yaojun Jiang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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Oh J, Kim B, Oh G, Hwangbo Y, Ye JC. End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography. Endocrinol Metab (Seoul) 2024; 39:500-510. [PMID: 38721637 PMCID: PMC11220219 DOI: 10.3803/enm.2023.1860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 02/19/2024] [Accepted: 03/05/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGRUOUND Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA). METHODS The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae. RESULTS Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson's r of 0.852 (P<0.001), and R2 of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson's r of 0.907 (P<0.001), and R2 of 0.781. CONCLUSION CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions.
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Affiliation(s)
- Jieun Oh
- Healthcare AI Team, National Cancer Center, Goyang, Korea
| | - Boah Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Gyutaek Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Yul Hwangbo
- Healthcare AI Team, National Cancer Center, Goyang, Korea
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
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He Y, Lin J, Zhu S, Zhu J, Xu Z. Deep learning in the radiologic diagnosis of osteoporosis: a literature review. J Int Med Res 2024; 52:3000605241244754. [PMID: 38656208 PMCID: PMC11044779 DOI: 10.1177/03000605241244754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024] Open
Abstract
OBJECTIVE Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. METHODS We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. RESULTS A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). CONCLUSIONS Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.
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Affiliation(s)
- Yu He
- Suzhou Medical College, Soochow University, Suzhou, Jiangsu, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Zhonghua Xu
- Department of Orthopedics, Jintan Affiliated Hospital to Jiangsu University, Changzhou, China
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Pan J, Lin PC, Gong SC, Wang Z, Cao R, Lv Y, Zhang K, Wang L. Effectiveness of opportunistic osteoporosis screening on chest CT using the DCNN model. BMC Musculoskelet Disord 2024; 25:176. [PMID: 38413868 PMCID: PMC10898023 DOI: 10.1186/s12891-024-07297-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 02/21/2024] [Indexed: 02/29/2024] Open
Abstract
OBJECTIVE To develop and evaluate a deep learning model based on chest CT that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images, and explore the feasibility and effectiveness of the model based on the lumbar 1 vertebral body alone. MATERIALS AND METHODS The chest CT images of 1048 health check subjects from January 2021 to June were retrospectively collected as the internal dataset (the segmentation model: 548 for training, 100 for tuning and 400 for test. The classification model: 530 for training, 100 for validation and 418 for test set). The subjects were divided into three categories according to the quantitative CT measurements, namely, normal, osteopenia and osteoporosis. First, a deep learning-based segmentation model was constructed, and the dice similarity coefficient(DSC) was used to compare the consistency between the model and manual labelling. Then, two classification models were established, namely, (i) model 1 (fusion feature construction of lumbar vertebral bodies 1 and 2) and (ii) model 2 (feature construction of lumbar 1 alone). Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of the models, and the Delong test was used to compare the areas under the curve. RESULTS When the number of images in the training set was 300, the DSC value was 0.951 ± 0.030 in the test set. The results showed that the model 1 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.990, 0.952 and 0.980; the model 2 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.983, 0.940 and 0.978. The Delong test showed that there was no significant difference in area under the curve (AUC) values between the osteopenia group and osteoporosis group (P = 0.210, 0.546), while the AUC value of normal model 2 was higher than that of model 1 (0.990 vs. 0.983, P = 0.033). CONCLUSION This study proposed a chest CT deep learning model that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images. We further constructed the comparable model based on the lumbar 1 vertebra alone which can shorten the scan length, reduce the radiation dose received by patients, and reduce the training cost of technologists.
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Affiliation(s)
- Jing Pan
- Department of Radiology, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210000, China
| | - Peng-Cheng Lin
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China
| | - Shen-Chu Gong
- Department of Radiology, The First People's Hospital of Nantong/The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, China
| | - Ze Wang
- Department of Radiology, The First People's Hospital of Nantong/The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, China
| | - Rui Cao
- Department of Radiology, The First People's Hospital of Nantong/The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, China
| | - Yuan Lv
- Department of Radiology, The First People's Hospital of Nantong/The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, China
| | - Kun Zhang
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, 226001, China.
| | - Lin Wang
- Department of Radiology, The First People's Hospital of Nantong/The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, China.
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Ong W, Liu RW, Makmur A, Low XZ, Sng WJ, Tan JH, Kumar N, Hallinan JTPD. Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography. Bioengineering (Basel) 2023; 10:1364. [PMID: 38135954 PMCID: PMC10741220 DOI: 10.3390/bioengineering10121364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Ren Wei Liu
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Weizhong Jonathan Sng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Constant C, Aubin CE, Kremers HM, Garcia DVV, Wyles CC, Rouzrokh P, Larson AN. The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications. NORTH AMERICAN SPINE SOCIETY JOURNAL 2023; 15:100236. [PMID: 37599816 PMCID: PMC10432249 DOI: 10.1016/j.xnsj.2023.100236] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/14/2023] [Indexed: 08/22/2023]
Abstract
Background Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging. Methods This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest. Results A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated. Conclusions Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.
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Affiliation(s)
- Caroline Constant
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
- AO Research Institute Davos, Clavadelerstrasse 8, CH 7270, Davos, Switzerland
| | - Carl-Eric Aubin
- Polytechnique Montreal, 2500 Chem. de Polytechnique, Montréal, QC H3T 1J4, Canada
| | - Hilal Maradit Kremers
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Diana V. Vera Garcia
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
| | - Cody C. Wyles
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Pouria Rouzrokh
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Radiology Informatics Laboratory, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
| | - Annalise Noelle Larson
- Orthopedic Surgery AI Laboratory, Mayo Clinic, 200 1st St Southwest, Rochester, MN, 55902, United States
- Department of Orthopedic Surgery, Mayo Clinic, 200, 1st St Southwest, Rochester, MN, 55902, United States
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End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:3018320. [PMID: 36970245 PMCID: PMC10036193 DOI: 10.1155/2023/3018320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 02/23/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.
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Dzierżak R, Omiotek Z. Application of Deep Convolutional Neural Networks in the Diagnosis of Osteoporosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8189. [PMID: 36365886 PMCID: PMC9655338 DOI: 10.3390/s22218189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
The aim of this study was to assess the possibility of using deep convolutional neural networks (DCNNs) to develop an effective method for diagnosing osteoporosis based on CT images of the spine. The research material included the CT images of L1 spongy tissue belonging to 100 patients (50 healthy and 50 diagnosed with osteoporosis). Six pre-trained DCNN architectures with different topological depths (VGG16, VGG19, MobileNetV2, Xception, ResNet50, and InceptionResNetV2) were used in the study. The best results were obtained for the VGG16 model characterised by the lowest topological depth (ACC = 95%, TPR = 96%, and TNR = 94%). A specific challenge during the study was the relatively small (for deep learning) number of observations (400 images). This problem was solved using DCNN models pre-trained on a large dataset and a data augmentation technique. The obtained results allow us to conclude that the transfer learning technique yields satisfactory results during the construction of deep models for the diagnosis of osteoporosis based on small datasets of CT images of the spine.
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Madireddy I, Wu T. Rule and Neural Network-Based Image Segmentation of Mice Vertebrae Images. Cureus 2022; 14:e27247. [PMID: 36039207 PMCID: PMC9401637 DOI: 10.7759/cureus.27247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2022] [Indexed: 12/03/2022] Open
Abstract
Background Image segmentation is a fundamental technique that allows researchers to process images from various sources into individual components for certain applications, such as visual or numerical evaluations. Image segmentation is beneficial when studying medical images for healthcare purposes. However, existing semantic image segmentation models like the U-net are computationally intensive. This work aimed to develop less complicated models that could still accurately segment images. Methodology Rule-based and linear layer neural network models were developed in Mathematica and trained on mouse vertebrae micro-computed tomography scans. These models were tasked with segmenting the cortical shell from the whole bone image. A U-net model was also set up for comparison. Results It was found that the linear layer neural network had comparable accuracy to the U-net model in segmenting the mice vertebrae scans. Conclusions This work provides two separate models that allow for automated segmentation of mouse vertebral scans, which could be potentially valuable in applications such as pre-processing the murine vertebral scans for further evaluations of the effect of drug treatment on bone micro-architecture.
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Wani IM, Arora S. Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey. Med Biol Eng Comput 2020; 58:1873-1917. [PMID: 32583141 DOI: 10.1007/s11517-020-02171-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 03/26/2020] [Indexed: 12/18/2022]
Abstract
Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.
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Affiliation(s)
- Insha Majeed Wani
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India
| | - Sakshi Arora
- School of Computer Science and Engineering, SMVDU, Katra, J&K, India.
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