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Yao J, Lim J, Lim GYS, Ong JCL, Ke Y, Tan TF, Tan TE, Vujosevic S, Ting DSW. Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema. EYE AND VISION (LONDON, ENGLAND) 2024; 11:23. [PMID: 38880890 PMCID: PMC11181581 DOI: 10.1186/s40662-024-00389-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/09/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. MAIN TEXT This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. CONCLUSION AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.
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Affiliation(s)
- Jie Yao
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Joshua Lim
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Gilbert Yong San Lim
- Duke-NUS Medical School, Singapore, Singapore
- SingHealth AI Health Program, Singapore, Singapore
| | - Jasmine Chiat Ling Ong
- Duke-NUS Medical School, Singapore, Singapore
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore
| | - Yuhe Ke
- Department of Anesthesiology and Perioperative Science, Singapore General Hospital, Singapore, Singapore
| | - Ting Fang Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Eye Clinic, IRCCS MultiMedica, Milan, Italy
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- SingHealth AI Health Program, Singapore, Singapore.
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Zhang L, Huang Y, Chen J, Xu X, Xu F, Yao J. Multimodal deep transfer learning to predict retinal vein occlusion macular edema recurrence after anti-VEGF therapy. Heliyon 2024; 10:e29334. [PMID: 38655307 PMCID: PMC11036002 DOI: 10.1016/j.heliyon.2024.e29334] [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: 04/05/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024] Open
Abstract
Purpose To develop a multimodal deep transfer learning (DTL) fusion model using optical coherence tomography angiography (OCTA) images to predict the recurrence of retinal vein occlusion (RVO) and macular edema (ME) after three consecutive anti-VEGF therapies. Methods This retrospective cross-sectional study consisted of 2800 B-scan OCTA macular images collected from 140 patients with RVO-ME. The central macular thickness (CMT) > 250 μm was used as a criterion for recurrence in the three-month follow-up after three injections of anti-VEGF therapy. The qualified OCTA image preprocessing and the lesion area segmentation were performed by senior ophthalmologists. We developed and validated the clinical, DTL, and multimodal fusion models based on clinical and extracted OCTA imaging features. The performance of the models and experts predictions were evaluated using several performance metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results The DTL models exhibited higher prediction efficacy than the clinical models and experts' predictions. Among the DTL models, the Vgg19 performed better than that of the other models, with an AUC of 0.968 (95 % CI, 0.943-0.994), accuracy of 0.913, sensitivity of 0.922, and specificity of 0.902 in the validation cohort. Moreover, the fusion Vgg19 model showed the highest prediction efficacy among all the models, with an AUC of 0.972 (95 % CI, 0.946-0.997), accuracy of 0.935, sensitivity of 0.935, and specificity of 0.934 in the validation cohort. Conclusions Multimodal fusion DTL models showed robust performance in predicting RVO-ME recurrence and may be applied to assist clinicians in determining patients' follow-up time after anti-VEGF therapy.
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Affiliation(s)
- Laihe Zhang
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Ying Huang
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Jiaqin Chen
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Xiangzhong Xu
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Fan Xu
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Jin Yao
- The Affiliated Eye Hospital, Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
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Darooei R, Nazari M, Kafieh R, Rabbani H. Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms. Diagnostics (Basel) 2023; 13:1994. [PMID: 37370889 DOI: 10.3390/diagnostics13121994] [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: 04/09/2023] [Revised: 05/22/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
The retina is a thin, light-sensitive membrane with a multilayered structure found in the back of the eyeball. There are many types of retinal disorders. The two most prevalent retinal illnesses are Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). Optical Coherence Tomography (OCT) is a vital retinal imaging technology. X-lets (such as curvelet, DTCWT, contourlet, etc.) have several benefits in image processing and analysis. They can capture both local and non-local features of an image simultaneously. The aim of this paper is to propose an optimal deep learning architecture based on sparse basis functions for the automated segmentation of cystic areas in OCT images. Different X-let transforms were used to produce different network inputs, including curvelet, Dual-Tree Complex Wavelet Transform (DTCWT), circlet, and contourlet. Additionally, three different combinations of these transforms are suggested to achieve more accurate segmentation results. Various metrics, including Dice coefficient, sensitivity, false positive ratio, Jaccard index, and qualitative results, were evaluated to find the optimal networks and combinations of the X-let's sub-bands. The proposed network was tested on both original and noisy datasets. The results show the following facts: (1) contourlet achieves the optimal results between different combinations; (2) the five-channel decomposition using high-pass sub-bands of contourlet transform achieves the best performance; and (3) the five-channel decomposition using high-pass sub-bands formations out-performs the state-of-the-art methods, especially in the noisy dataset. The proposed method has the potential to improve the accuracy and speed of the segmentation process in clinical settings, facilitating the diagnosis and treatment of retinal diseases.
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Affiliation(s)
- Reza Darooei
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Milad Nazari
- Department of Molecular Biology and Genetics, Aarhus University, 8200 Aarhus, Denmark
- The Danish Research Institute of Translational Neuroscience (DANDRITE), Aarhus University, 8200 Aarhus, Denmark
| | - Rahele Kafieh
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
- Department of Engineering, Durham University, South Road, Durham DH1 3RW, UK
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
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Yoshida Y, Hayashi Y, Shimada T, Hattori N, Tomita K, Miura RE, Yamao Y, Tateishi S, Iwadate Y, Nakada TA. Prehospital stroke-scale machine-learning model predicts the need for surgical intervention. Sci Rep 2023; 13:9135. [PMID: 37277424 DOI: 10.1038/s41598-023-36004-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/27/2023] [Indexed: 06/07/2023] Open
Abstract
While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.
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Affiliation(s)
- Yoichi Yoshida
- Department of Neurosurgery, Chiba Municipal Kaihin Hospital, Chiba, Japan
- Department of Neurological Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yosuke Hayashi
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
| | - Tadanaga Shimada
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
| | - Noriyuki Hattori
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
| | - Keisuke Tomita
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
| | - Rie E Miura
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
- SMART119 Inc., 7th Floor, Chiba Chuo Twin Building No. 2, 2-5-1 Chuo, Chiba, Japan
| | - Yasuo Yamao
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
- SMART119 Inc., 7th Floor, Chiba Chuo Twin Building No. 2, 2-5-1 Chuo, Chiba, Japan
| | - Shino Tateishi
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan
- SMART119 Inc., 7th Floor, Chiba Chuo Twin Building No. 2, 2-5-1 Chuo, Chiba, Japan
| | - Yasuo Iwadate
- Department of Neurological Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Taka-Aki Nakada
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan.
- SMART119 Inc., 7th Floor, Chiba Chuo Twin Building No. 2, 2-5-1 Chuo, Chiba, Japan.
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Melo T, Carneiro Â, Campilho A, Mendonça AM. Retinal layer and fluid segmentation in optical coherence tomography images using a hierarchical framework. J Med Imaging (Bellingham) 2023; 10:014006. [PMID: 36825083 PMCID: PMC9941746 DOI: 10.1117/1.jmi.10.1.014006] [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: 08/05/2022] [Accepted: 01/27/2023] [Indexed: 02/23/2023] Open
Abstract
Purpose The development of accurate methods for retinal layer and fluid segmentation in optical coherence tomography images can help the ophthalmologists in the diagnosis and follow-up of retinal diseases. Recent works based on joint segmentation presented good results for the segmentation of most retinal layers, but the fluid segmentation results are still not satisfactory. We report a hierarchical framework that starts by distinguishing the retinal zone from the background, then separates the fluid-filled regions from the rest, and finally, discriminates the several retinal layers. Approach Three fully convolutional networks were trained sequentially. The weighting scheme used for computing the loss function during training is derived from the outputs of the networks trained previously. To reinforce the relative position between retinal layers, the mutex Dice loss (included for optimizing the last network) was further modified so that errors between more "distant" layers are more penalized. The method's performance was evaluated using a public dataset. Results The proposed hierarchical approach outperforms previous works in the segmentation of the inner segment ellipsoid layer and fluid (Dice coefficient = 0.95 and 0.82, respectively). The results achieved for the remaining layers are at a state-of-the-art level. Conclusions The proposed framework led to significant improvements in fluid segmentation, without compromising the results in the retinal layers. Thus, its output can be used by ophthalmologists as a second opinion or as input for automatic extraction of relevant quantitative biomarkers.
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Affiliation(s)
- Tânia Melo
- University of Porto, Electrical and Computer Engineering Department, Faculty of Engineering, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Ângela Carneiro
- University of Porto, Surgery and Physiology Department, Faculty of Medicine, Porto, Portugal
- University Hospital Center of São João, Porto, Portugal
| | - Aurélio Campilho
- University of Porto, Electrical and Computer Engineering Department, Faculty of Engineering, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
| | - Ana Maria Mendonça
- University of Porto, Electrical and Computer Engineering Department, Faculty of Engineering, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal
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Multi-layer segmentation of retina OCT images via advanced U-net architecture. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Quek TC, Takahashi K, Kang HG, Thakur S, Deshmukh M, Tseng RMWW, Nguyen H, Tham YC, Rim TH, Kim SS, Yanagi Y, Liew G, Cheng CY. Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans. EPMA J 2022; 13:547-560. [PMID: 36505893 PMCID: PMC9727042 DOI: 10.1007/s13167-022-00301-5] [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: 08/29/2022] [Accepted: 10/25/2022] [Indexed: 11/21/2022]
Abstract
Aims Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications. Methods The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets. Results Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index. Conclusion We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting. Supplementary information The online version contains supplementary material available at 10.1007/s13167-022-00301-5.
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Affiliation(s)
- Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | | | - Hyun Goo Kang
- Department of Ophthalmology, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sahil Thakur
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | - Mihir Deshmukh
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | - Rachel Marjorie Wei Wen Tseng
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
| | - Helen Nguyen
- Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
- School of Optometry and Vision Science, Faculty of Science, The University of New South Wales, Sydney, NSW Australia
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Medi Whale Inc, Seoul, South Korea
| | - Sung Soo Kim
- Department of Ophthalmology, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yasuo Yanagi
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology and Microtechnology, Yokohama City University, Yokohama, Japan
| | - Gerald Liew
- Department of Ophthalmology, Centre for Vision Research, Westmead Institute for Medical Research, University of Sydney, Sydney, Australia
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, The Academia, 20 College Rd, Level 6 Discovery Tower, Singapore, 169856 Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
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Wu Y, Ghoraani B. Biological Signal Processing and Analysis for Healthcare Monitoring. SENSORS 2022; 22:s22145341. [PMID: 35891021 PMCID: PMC9319153 DOI: 10.3390/s22145341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 11/16/2022]
Affiliation(s)
- Yunfeng Wu
- School of Informatics, Xiamen University, 422 Si Ming South Road, Xiamen 361005, China
- Correspondence:
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA;
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Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography. SENSORS 2022; 22:s22145230. [PMID: 35890909 PMCID: PMC9324543 DOI: 10.3390/s22145230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 02/04/2023]
Abstract
Automatically delineating the deep and superficial aponeurosis of the skeletal muscles from ultrasound images is important in many aspects of the clinical routine. In particular, finding muscle parameters, such as thickness, fascicle length or pennation angle, is a time-consuming clinical task requiring both human labour and specialised knowledge. In this study, a multi-step solution for automating these tasks is presented. A process to effortlessly extract the aponeurosis for automatically measuring the muscle thickness has been introduced as a first step. This process consists mainly of three parts. In the first part, the Attention UNet has been incorporated to automatically delineate the boundaries of the studied muscles. Afterwards, a specialised post-processing algorithm was utilised to improve (and correct) the segmentation results. Lastly, the calculation of the muscle thickness was performed. The proposed method has achieved similar to a human-level performance. In particular, the overall discrepancy between the automatic and the manual muscle thickness measurements was equal to 0.4 mm, a significant result that demonstrates the feasibility of automating this task. In the second step of the proposed methodology, the fascicle’s length and pennation angle are extracted through an unsupervised pipeline. Initially, filtering is applied to the ultrasound images to further distinguish the tissues from the other muscle structures. Later, the well-known K-Means algorithm is used to isolate them successfully. As the last step, the dominant angle of the segmented muscle tissues is reported and compared with manual measurements. The proposed pipeline is showing very promising results in the evaluated dataset. Specifically, in the calculation of the pennation angle, the overall discrepancy between the automatic and the manual measurements was less than 2.22° (degrees), once more comparable with the human-level performance. Finally, regarding the fascicle length measurements, the results were divided based on the muscle properties. In the muscles where a large portion (or all) of the fascicles are located between the upper and lower aponeuroses, the proposed pipeline exhibits superb performance; otherwise, overall accuracy deteriorates due to errors caused by the trigonometric approximations needed for the length calculation.
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