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Ma D, Wang Y, Zhang X, Su D, Ma M, Qian B, Yang X, Gao J, Wu Y. 3D U-Net Neural Network Architecture-Assisted LDCT to Acquire Vertebral Morphology Parameters: A Vertebral Morphology Comprehensive Analysis in a Chinese Population. Calcif Tissue Int 2024:10.1007/s00223-024-01255-8. [PMID: 39017691 DOI: 10.1007/s00223-024-01255-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 07/01/2024] [Indexed: 07/18/2024]
Abstract
To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.
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Affiliation(s)
- Duoshan Ma
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Yan Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Xinxin Zhang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Danyang Su
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Mengze Ma
- Medical 3D Printing Center, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Baoxin Qian
- Dongsheng Science and Technology Park, Room A206, B2, Huiying Medical Technology Co, Ltd, HaiDian District, Beijing City, 100192, China
| | - Xiaopeng Yang
- Medical 3D Printing Center, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China
| | - Yan Wu
- Medical 3D Printing Center, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China.
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Lee S, Jung JY, Mahatthanatrakul A, Kim JS. Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances. Neurospine 2024; 21:474-486. [PMID: 38955525 PMCID: PMC11224760 DOI: 10.14245/ns.2448388.194] [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: 04/16/2024] [Revised: 05/14/2024] [Accepted: 05/23/2024] [Indexed: 07/04/2024] Open
Abstract
Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.
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Affiliation(s)
- Sungwon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Akaworn Mahatthanatrakul
- Department of Orthopaedics, Faculty of Medicine, Naresuan University Hospital, Phitsanulok, Thailand
| | - Jin-Sung Kim
- Spine Center, Department of Neurosurgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Yıldız Potter İ, Rodriguez EK, Wu J, Nazarian A, Vaziri A. An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01135-5. [PMID: 38717516 DOI: 10.1007/s10278-024-01135-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 06/29/2024]
Abstract
Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.8B. With an aging population, the rate of osteoporotic VCFs and their associated burdens are expected to rise. Those burdens include pain, functional impairment, and increased medical expenditure. Therefore, it is of utmost importance to develop an analytical tool to aid in the identification of VCFs. Computed Tomography (CT) imaging is commonly used to detect occult injuries. Unlike the existing VCF detection approaches based on CT, the standard clinical criteria for determining VCF relies on the shape of vertebrae, such as loss of vertebral body height. We developed a novel automated vertebrae localization, segmentation, and osteoporotic VCF detection pipeline for CT scans using state-of-the-art deep learning models to bridge this gap. To do so, we employed a publicly available dataset of spine CT scans with 325 scans annotated for segmentation, 126 of which also graded for VCF (81 with VCFs and 45 without VCFs). Our approach attained 96% sensitivity and 81% specificity in detecting VCF at the vertebral-level, and 100% accuracy at the subject-level, outperforming deep learning counterparts tested for VCF detection without segmentation. Crucially, we showed that adding predicted vertebrae segments as inputs significantly improved VCF detection at both vertebral and subject levels by up to 14% Sensitivity and 20% Specificity (p-value = 0.028).
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Affiliation(s)
| | - Edward K Rodriguez
- Carl J. Shapiro Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center (BIDMC), Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, RN123, Boston, MA, 02215, USA
| | - Jim Wu
- Department of Radiology, Beth Israel Deaconess Medical Center (BIDMC), Harvard Medical School, 330 Brookline Avenue, Shapiro 4, Boston, MA, 02215, USA
| | - Ara Nazarian
- Carl J. Shapiro Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center (BIDMC), Harvard Medical School, 330 Brookline Avenue, Stoneman 10, Boston, MA, 02215, USA
- Musculoskeletal Translational Innovation Initiative, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, RN123, Boston, MA, 02215, USA
- Department of Orthopaedics Surgery, Yerevan State University, Yerevan, Armenia
| | - Ashkan Vaziri
- BioSensics, LLC, 57 Chapel Street, Newton, MA, 02458, USA
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Johnson GW, Chanbour H, Ali MA, Chen J, Metcalf T, Doss D, Younus I, Jonzzon S, Roth SG, Abtahi AM, Stephens BF, Zuckerman SL. Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May Be Necessary for Accurate Models. Spine (Phila Pa 1976) 2023; 48:1688-1695. [PMID: 37644737 PMCID: PMC11101214 DOI: 10.1097/brs.0000000000004816] [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: 05/29/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
STUDY DESIGN Retrospective cohort. OBJECTIVE In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: (1) traditional demographics and radiographic measurements, (2) raw preoperative scoliosis radiographs, and (3) raw preoperative thoracic magnetic resonance imaging (MRI). SUMMARY OF BACKGROUND DATA Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict. MATERIALS AND METHODS A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009 to 2021. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For model 1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for model 2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for model 3, a CNN was trained on raw preoperative thoracic T1 MRIs. RESULTS A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model 1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity: 57.2% and a specificity: 56.3%. Model 2: a CNN with raw scoliosis x-rays predicted PJK with a sensitivity: 68.2% and specificity: 58.3%. Model 3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by model 3 elucidated that soft tissue features predominated all true positive PJK predictions. CONCLUSIONS The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.
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Affiliation(s)
| | - Hani Chanbour
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Mir Amaan Ali
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Jeffrey Chen
- Vanderbilt University School of Medicine, Nashville, TN
| | - Tyler Metcalf
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Doss
- Vanderbilt University School of Medicine, Nashville, TN
| | - Iyan Younus
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Soren Jonzzon
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Steven G. Roth
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Amir M. Abtahi
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Byron F. Stephens
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Scott L. Zuckerman
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN
- Department of Orthopedic Surgery, Vanderbilt University Medical Center, Nashville, TN
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Suri A, Tang S, Kargilis D, Taratuta E, Kneeland BJ, Choi G, Agarwal A, Anabaraonye N, Xu W, Parente JB, Terry A, Kalluri A, Song K, Rajapakse CS. Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis. Radiol Artif Intell 2023; 5:e220158. [PMID: 37529207 PMCID: PMC10388214 DOI: 10.1148/ryai.220158] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 05/16/2023] [Accepted: 06/05/2023] [Indexed: 08/03/2023]
Abstract
Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005-2020). The algorithm measured Cobb angles on hold-out internal (n = 460) and external (n = 161) test sets with less than 2° error (intraclass correlation coefficient, 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in less than 0.5 second, did not differ significantly (P = .05 cutoff) from ground truth measurements, regardless of the presence or absence of surgical hardware (P = .80), age (P = .58), sex (P = .83), body mass index (P = .63), scoliosis severity (P = .44), or image type (low-dose stereoradiographic image vs radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients. Keywords: Cobb Angle, Convolutional Neural Network, Deep Learning Algorithms, Pediatrics, Machine Learning Algorithms, Scoliosis, Spine Supplemental material is available for this article. © RSNA, 2023.
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Kuang X, Cheung JP, Huang T, Zhang T. SpineQ: Unsupervised 3D Lumbar Quantitative Assessment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38557307 DOI: 10.1109/embc40787.2023.10485565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Most lumbar quantitative assessment methods can only analyze the image from one view and require laborious manual annotation. We aim to develop an unsupervised pipeline for 3D quantitative assessment of the lumbar spine that can assess the MRI with different views. We combine rule-based and deep learning methods to generate multi-tissue segmentation, and parameters can be measured from segmentation results using the anatomical and geometric prior. Preliminary testing demonstrates that our proposed method can generate accurate segmentation and measurement results.Clinical Relevance- The proposed unsupervised 3D lumbar quantitative assessment pipeline can significantly improve the efficiency and consistency of clinical diagnosis and surgical planning.
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Zhang S, Zhao Z, Qiu L, Liang D, Wang K, Xu J, Zhao J, Sun J. Automatic vertebral fracture and three-column injury diagnosis with fracture visualization by a multi-scale attention-guided network. Med Biol Eng Comput 2023:10.1007/s11517-023-02805-2. [PMID: 36848011 DOI: 10.1007/s11517-023-02805-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/08/2023] [Indexed: 03/01/2023]
Abstract
Deep learning methods have the potential to improve the efficiency of diagnosis for vertebral fractures with computed tomography (CT) images. Most existing intelligent vertebral fracture diagnosis methods only provide dichotomized results at a patient level. However, a fine-grained and more nuanced outcome is clinically needed. This study proposed a novel network, a multi-scale attention-guided network (MAGNet), to diagnose vertebral fractures and three-column injuries with fracture visualization at a vertebra level. By imposing attention constraints through a disease attention map (DAM), a fusion of multi-scale spatial attention maps, the MAGNet can get task highly relevant features and localize fractures. A total of 989 vertebrae were studied here. After four-fold cross-validation, the area under the ROC curve (AUC) of our model for vertebral fracture dichotomized diagnosis and three-column injury diagnosis was 0.884 ± 0.015 and 0.920 ± 0.104, respectively. The overall performance of our model outperformed classical classification models, attention models, visual explanation methods, and attention-guided methods based on class activation mapping. Our work can promote the clinical application of deep learning to diagnose vertebral fractures and provide a way to visualize and improve the diagnosis results with attention constraints.
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Affiliation(s)
- Shunan Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Ziqi Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Lu Qiu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Duan Liang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kun Wang
- Renji Hospital, Shanghai, 200127, China
| | - Jun Xu
- Shanghai Sixth People's Hospital, Shanghai, 200233, China.
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Jianqi Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Performance of a deep convolutional neural network for MRI-based vertebral body measurements and insufficiency fracture detection. Eur Radiol 2022; 33:3188-3199. [PMID: 36576545 PMCID: PMC10121505 DOI: 10.1007/s00330-022-09354-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/23/2022] [Accepted: 11/29/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVES The aim is to validate the performance of a deep convolutional neural network (DCNN) for vertebral body measurements and insufficiency fracture detection on lumbar spine MRI. METHODS This retrospective analysis included 1000 vertebral bodies in 200 patients (age 75.2 ± 9.8 years) who underwent lumbar spine MRI at multiple institutions. 160/200 patients had ≥ one vertebral body insufficiency fracture, 40/200 had no fracture. The performance of the DCNN and that of two fellowship-trained musculoskeletal radiologists in vertebral body measurements (anterior/posterior height, extent of endplate concavity, vertebral angle) and evaluation for insufficiency fractures were compared. Statistics included (a) interobserver reliability metrics using intraclass correlation coefficient (ICC), kappa statistics, and Bland-Altman analysis, and (b) diagnostic performance metrics (sensitivity, specificity, accuracy). A statistically significant difference was accepted if the 95% confidence intervals did not overlap. RESULTS The inter-reader agreement between radiologists and the DCNN was excellent for vertebral body measurements, with ICC values of > 0.94 for anterior and posterior vertebral height and vertebral angle, and good to excellent for superior and inferior endplate concavity with ICC values of 0.79-0.85. The performance of the DCNN in fracture detection yielded a sensitivity of 0.941 (0.903-0.968), specificity of 0.969 (0.954-0.980), and accuracy of 0.962 (0.948-0.973). The diagnostic performance of the DCNN was independent of the radiological institution (accuracy 0.964 vs. 0.960), type of MRI scanner (accuracy 0.957 vs. 0.964), and magnetic field strength (accuracy 0.966 vs. 0.957). CONCLUSIONS A DCNN can achieve high diagnostic performance in vertebral body measurements and insufficiency fracture detection on heterogeneous lumbar spine MRI. KEY POINTS • A DCNN has the potential for high diagnostic performance in measuring vertebral bodies and detecting insufficiency fractures of the lumbar spine.
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Hayashi D. Multitask Deep Learning Tools Are Needed for Clinical Practice-Especially for Low Back Pain. Radiology 2022; 305:167-168. [PMID: 35699583 DOI: 10.1148/radiol.221202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Daichi Hayashi
- From the Department of Radiology, Stony Brook University Renaissance School of Medicine, State University of New York, 101 Nicolls Rd, HSc Level 4, Room 120, Stony Brook, NY 11794-8460
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Mushtaq M, Akram MU, Alghamdi NS, Fatima J, Masood RF. Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models. SENSORS (BASEL, SWITZERLAND) 2022; 22:1547. [PMID: 35214448 PMCID: PMC8879729 DOI: 10.3390/s22041547] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 12/30/2022]
Abstract
The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis. Our paper presents the methods that will help clinicians to grade the severity of the disease with confidence, as the current manual diagnosis by different doctors has dissimilarity and variations in the analysis of diseases. In this paper we discuss the lumbar spine localization and segmentation which help for the analysis of lumbar spine deformities. The lumber spine is localized using YOLOv5 which is the fifth variant of the YOLO family. It is the fastest and the lightest object detector. Mean average precision (mAP) of 0.975 is achieved by YOLOv5. To diagnose the lumbar lordosis, we correlated the angles with region area that is computed from the YOLOv5 centroids and obtained 74.5% accuracy. Cropped images from YOLOv5 bounding boxes are passed through HED U-Net, which is a combination of segmentation and edge detection frameworks, to obtain the segmented vertebrae and its edges. Lumbar lordortic angles (LLAs) and lumbosacral angles (LSAs) are found after detecting the corners of vertebrae using a Harris corner detector with very small mean errors of 0.29° and 0.38°, respectively. This paper compares the different object detectors used to localize the vertebrae, the results of two methods used to diagnose the lumbar deformity, and the results with other researchers.
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Affiliation(s)
- Malaika Mushtaq
- Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; (M.M.); (M.U.A.)
| | - Muhammad Usman Akram
- Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; (M.M.); (M.U.A.)
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Joddat Fatima
- Department of Computer Science, Bahria University, Islamabad 44000, Pakistan;
| | - Rao Farhat Masood
- Department of Electrical Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan;
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