A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images

• Autorzy: Khanna Anita , Londhe Narendra D. , Gupta S. , Semwal Ashish

Identyfikatory

DOI

10.1016/j.bbe.2020.07.007

• Języki publikacji: EN

Abstrakty

EN

To improve the early diagnosis and treatment of lung diseases automated lung segmentation from CT images is a crucial task for clinical decision. The segmentation of the lung region from the CT scans is a very challenging task due to the irregular shape and size of lungs, low contrast and fuzzy boundaries of the lung. The manual segmentation of lung CT images is a laborious task. Therefore, various approaches are suggested by the researcher in the recent past for the automated lung segmentation. However, the existing approaches either utilize low-level handcraft features or CNN based Fully Convolutional Networks. The low-level hand-craft feature-based approaches lead to poor generalization, while the shallower networks are unable to extract more discriminative features. Hence, in this study, we have implemented a deep learning-based architecture called Residual U-Net with a false-positive removal algorithm for lung CT segmentation. Here, we have suggested that learning from a substantially deeper network with residual units can extract more discriminative feature representation as compared to shallow network for lung segmentation. To take full advantage of the deeper network, we have utilized a set of schemes to ensure efficient training. First, we have implemented a U-Net architecture with residual block to overcome the problem of performance degradation. Further, various data augmentation techniques are utilized to improve the generalization capability of the proposed method. The experimental results show that the proposed method achieved competitive results over the existing methods with DSC of 98.63%, 99.62% and 98.68% for LUNA16, VESSEL12 and HUG-ILD dataset respectively.

Słowa kluczowe

EN

lung segmentation; residual learning; residual U-Net; data augmentation; deep learning

PL

segmentacja płuc; rozszerzanie danych; uczenie głębokie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 3
• Strony: 1314--1327
• Opis fizyczny: Bibliogr.32 poz., rys., tab., wykr.

Twórcy

autor

Khanna Anita

• Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India

autor

Londhe Narendra D.

• Department of Electrical Engineering, NIT Raipur, Chhattisgarh 492010, India

autor

Gupta S.

• Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India

autor

Semwal Ashish

• Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).