Automatic lung segmentation in dynamic thoracic MRI using two-stage deep convolutional neural networks

Lipeng Xie; Jayaram K. Udupa; Yubing Tong; Joseph M. McDonough; Caiyun Wu; Carina Lott; Jason B. Anari; Patrick J. Cahill; Drew A. Torigian

doi:10.1117/12.2612558

4 April 2022 Automatic lung segmentation in dynamic thoracic MRI using two-stage deep convolutional neural networks

Lipeng Xie, Jayaram K. Udupa, Yubing Tong, Joseph M. McDonough, Caiyun Wu, Carina Lott, Jason B. Anari, Patrick J. Cahill, Drew A. Torigian

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Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120323O (2022) https://doi.org/10.1117/12.2612558
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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Abstract

Lung segmentation in dynamic thoracic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders. Some semi-automatic and automatic lung segmentation methods based on traditional image processing models have been proposed mainly for CT with good performance. However, the low efficiency and robustness of these methods and inapplicability to dMRI make them unsuitable to segment the large numbers of dMRI datasets. In this paper, we present a novel automatic lung segmentation approach for dMRI based on two-stage convolutional neural networks (CNNs). In the first stage, we utilize the modified min-max normalization method to pre-process MRI for increasing the contrast between the lung and surrounding tissue and propose a corner-points and CNN based region of interest (ROI) detection strategy to extract the lung ROI from sagittal dMRI slices, which can reduce the negative influence of tissues located far away from the lung. In the second stage, we input the adjacent ROIs of target slices into the modified 2D U-Net to segment the lung tissue. The qualitative and quantitative results demonstrate that our approach achieves high accuracy and stability in terms of lung segmentation for dMRI.

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Lipeng Xie, Jayaram K. Udupa, Yubing Tong, Joseph M. McDonough, Caiyun Wu, Carina Lott, Jason B. Anari, Patrick J. Cahill, and Drew A. Torigian "Automatic lung segmentation in dynamic thoracic MRI using two-stage deep convolutional neural networks", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323O (4 April 2022); https://doi.org/10.1117/12.2612558

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