Download PDFOpen PDF in browserSegmentation Trachea and Bronchial Branches in Chest Computed Tomography Image by Deep Learning - preliminary results -7 pages•Published: January 16, 2022AbstractSegmentation is one of the most common methods for analyzing and processing medical images, assisting doctors in making accurate diagnoses by providing detailed information about the required body part. However, segmenting medical images presents a number of challenges, including the need for medical professionals to be trained, the fact that it is time-consuming and prone to errors. As a result, it appears that an automated medical image segmentation system is required. Deep learning algorithms have recently demonstrated superior performance for segmentation tasks, particularly semantic segmentation networks that provide a pixel-level understanding of images. U- Net for image segmentation is one of the modern complex networks in the field of medical imaging; several segmentation networks have been built on its foundation with the advancements of Recurrent Residual convolutional units and the construction of recurrent residual convolutional neural network based on U-Net (R2U-Net). R2U-Net is used to perform trachea and bronchial segmentation on a dataset of 36,000 images. With a variety of experiments, the proposed segmentation resulted in a dice-coefficient of 0.8394 on the test dataset. Finally, a number of research issues are raised, indicating the need for future improvements.Keyphrases: bronchial, deep learning, medical image, r2u net, segmentation, trachea In: Tich Thien Truong, Trung Nghia Tran, Thanh Nha Nguyen and Quoc Khai Le (editors). Proceedings of International Symposium on Applied Science 2021, vol 4, pages 109-115.
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