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Journal of Medical Signals and Sensors، جلد ۱۳، شماره ۴، صفحات ۲۵۳-۲۶۰
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عنوان انگلیسی Loss-Modified Transformer-Based U-Net for Accurate Segmentation of Fluids in Optical Coherence Tomography Images of Retinal Diseases
چکیده انگلیسی مقاله Background:Optical coherence tomography (OCT) imaging significantly contributes to ophthalmology in the diagnosis of retinal disorders such as age-related macular degeneration and diabetic macular edema. Both diseases involve the abnormal accumulation of fluids, location, and volume, which is vitally informative in detecting the severity of the diseases. Automated and accurate fluid segmentation in OCT images could potentially improve the current clinical diagnosis. This becomes more important by considering the limitations of manual fluid segmentation as a time-consuming and subjective to error method. Methods:Deep learning techniques have been applied to various image processing tasks, and their performance has already been explored in the segmentation of fluids in OCTs. This article suggests a novel automated deep learning method utilizing the U-Net structure as the basis. The modifications consist of the application of transformers in the encoder path of the U-Net with the purpose of more concentrated feature extraction. Furthermore, a custom loss function is empirically tailored to efficiently incorporate proper loss functions to deal with the imbalance and noisy images. A weighted combination of Dice loss, focal Tversky loss, and weighted binary cross-entropy is employed. Results: Different metrics are calculated. The results show high accuracy (Dice coefficient of 95.52) and robustness of the proposed method in comparison to different methods after adding extra noise to the images (Dice coefficient of 92.79). Conclusions: The segmentation of fluid regions in retinal OCT images is critical because it assists clinicians in diagnosing macular edema and executing therapeutic operations more quickly. This study suggests a deep learning framework and novel loss function for automated fluid segmentation of retinal OCT images with excellent accuracy and rapid convergence result.
کلیدواژه‌های انگلیسی مقاله Customized loss function,deep learning,fluid accumulation,optical coherence tomography,semantic segmentation
نویسندگان مقاله | Reza Darooei
3-Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark, United Kingdom 4-DANDRITE – The Danish Research Institute of Translational Neuroscience, Aarhus University, Aarhus, Denmark, United Kingdom


| Milad Nazari
1-Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 5-Department of Engineering, Durham University, Durham, United Kingdom


| Rahle Kafieh
1-Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 2-Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Hossein Rabbani


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/684
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Original Articles
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