Myocardium Segmentation using Two-Step Deep Learning with Smoothed Masks by Gaussian Blur
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Дата
2023-01-07
Автори
Slobodzian, Vitalii
Radiuk, Pavlo
Zingailo, Anastasiia
Barmak, Olexander
Krak, Iurii
Назва журналу
Номер ISSN
Назва тому
Видавець
CEUR.org
Анотація
Nowadays, cardiac magnetic resonance images face challenges in distinguishing between inflamed and non-inflamed tissues due to subtle color variations rather than clear density distinctions. Pixel values in these images vary based on individual subjects and the MRI equipment, making them inconsistent across different training datasets. Thus, detecting inflamed tissues in MRIs largely depends on the expertise of interpreting physicians, making it time-consuming and complicating the training of accurate classifiers. To address this issue, in this study, we propose a novel approach for myocardium segmentation on MRI images utilizing a two-stage neural network process coupled with mask refinement. The initial network outlines the myocardium, which is then fine-tuned by the second network for precise myocardium segmentation. A key enhancement involves mask post-processing via Gaussian blur, where the blur coefficient is automatically adjusted. Experimental outcomes demonstrated an increase in the Dice coefficient from 0.889 to 0.894 upon removing non-essential labels. Moreover, using a dual-model approach for myocardium localization and contour definition elevated the coefficient to 0.938. Employing the Gaussian blur during mask resizing culminated in an impressive average Dice coefficient of 0.955.
Опис
Ключові слова
Cardiac MRI, myocardium segmentation, medical image analysis, improved mask, deep learning, human-in-the-loop
Бібліографічний опис
Slobodzian V., Radiuk P., Zingailo A., Barmak O., Krak Iu. Myocardium segmentation using two-step deep learning with smoothed masks by Gaussian blur. The 6th International Conference on Informatics & Data-Driven Medicine (IDDM-2023) : CEUR-Workshop Proceedings. Vol. 3609. (Bratislava, Slovakia, 17-19 November 2023). CEUR-WS.org, Aachen, 2023. P. 77-91. URL: https://ceur-ws.org/Vol-3609/paper7.pdf