Lung segmentation plays a crucial role in computer-aided diagnosis using Chest Radiographs (CRs). We implement a U-Net architecture for lung segmentation in CRs across multiple publicly available datasets. We utilize a private dataset with 160 CRs provided by the Riverain Medical Group for training purposes. A publicly available dataset provided by the Japanese Radiological Scientific Technology (JRST) is used for testing. The active shape model-based results would serve as the ground truth for both these datasets. In addition, we also study the performance of our algorithm on a publicly available Shenzhen dataset which contains 566 CRs with manually segmented lungs (ground truth). Our overall performance in terms of pixel-based classification is about 98.3% and 95.6% for a set of 100 CRs in Shenzhen dataset and 140 CRs in JRST dataset. We also achieve an intersection over union value of 0.95 at a computation time of 8 seconds for the entire suite of Shenzhen testing cases.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Computationally Efficient U-Net Architecture for Lung Segmentation in Chest Radiographs


    Contributors:


    Publication date :

    2019-07-01


    Size :

    1104660 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Ensemble Method of Lung Segmentation in Chest Radiographs

    Narayanan, Barath Narayanan / De Silva, Manawduge Supun / Hardie, Russell C. et al. | IEEE | 2021


    Lung contour detection in chest radiographs using 1-D convolution neural networks

    Tsujii, O. / Freedman, M. T. / Mun, S. K. | British Library Online Contents | 1999



    Pre-Classification of Chest Radiographs for Improved Active Shape Model Segmentation of Ribs

    Ramachandran, J. / Pattichis, M. / Soliz, P. et al. | British Library Conference Proceedings | 2002


    Automatic segmentation of hand-wrist radiographs

    Manos, G. / Cairns, A. Y. / Ricketts, I. W. et al. | British Library Online Contents | 1993