view identification assisted fully convolutional network for lung field segmentation CORD-Papers-2021-10-25 (Version 1)

Title: View Identification Assisted Fully Convolutional Network for Lung Field Segmentation of Frontal and Lateral Chest Radiographs
Abstract: Locating lung field is a critical and fundamental processing stage in the automated analysis of chest radiographs (CXRs) for pulmonary disorders During the routine examination of CXRs, using both frontal and lateral CXRs can benefit clinical diagnosis of cardiothoracic and lung diseases However, the accurate segmentation of lung fields on both frontal and lateral CXRs is still challenging due to the blurry boundary of the lung field on lateral CXRs and the poor generalization ability of the models Existing deep learning-based methods focused on lung field segmentation on frontal CXRs, and the generalization ability of these methods on the different type of CXRs (e g , pediatric CXRs) and new lung diseases (e g , COVID-19) has not been tested In this paper, a view identification assisted fully convolutional network (VI-FCN) is proposed for the segmentation of lung fields on frontal and lateral CXRs simultaneously The VI-FCN consists of an FCN branch for lung field segmentation and a view identification branch for identification of the frontal and lateral CXRs and for enhancing the lung field segmentation To improve the generalization ability of VI-FCN, six public datasets and our frontal and lateral CXRs (over 2000 CXRs) were collected for training The segmentation of lung fields on the Japanese Society of Radiological Technology (JSRT) dataset yields mean dice similarity coefficient (DSC) of 0 979 ±0 008, mean Jaccard index (Ω) of 0 959 ±0 016, and mean boundary distance (MBD) of 1 023 ±0 487mm Besides, the VI-FCN achieves mean DSC of 0 973 ±0 010, mean Ωof 0 947 ±0 018, and mean MBD of 1 923 ±0 755mm for the segmentation of lung fields on our lateral CXRs The experiments demonstrate the superior performance of the proposed VI-FCN over most of existing state-of-the-art methods Moreover, the proposed VI-FCN achieves promising results on untrained pediatric CXRs and COVID-19 datasets CCBYNCND
Published: 2021
Journal: IEEE Access
Author Name: Xi, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xi_y
Author Name: Zhong, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhong_l
Author Name: Xie, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xie_w
Author Name: Qin, G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/qin_g
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Feng, Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/feng_q
Author Name: Yang, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yang_w
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #1210299
has_full_text: FALSE
G_ID: view_identification_assisted_fully_convolutional_network_for_lung_field_segmentation
S2 ID: 233375921