Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation

Abstract

Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable success in segmenting lungs from CXR images with normal or mildly abnormal findings, their performance declines when faced with complex structures, such as pulmonary opacifications. In this study, we propose AMRU++, an attention-based multi-residual UNet++ network designed for robust and accurate lung segmentation in CXR images with both normal and severe abnormalities. The model incorporates attention modules to capture relevant spatial information and multi-residual blocks to extract rich contextual and discriminative features of lung regions. To further enhance segmentation performance, we introduce a data augmentation technique that simulates the features and characteristics of CXR pathologies, addressing the issue of limited annotated data. Extensive experiments on public and private datasets comprising 350 cases of pneumoconiosis, COVID-19, and tuberculosis validate the effectiveness of our proposed framework and data augmentation technique.

Figure 1

Examples of chest X-ray images with symptoms of different lung disease; (a, b) Pneumoconiosis, (c) COVID-19 and (d) Tuberculosis. Red bounding boxes used to highlight the regions of blurred lung boundaries due to opacities.

Figure 2

(a) Architecture of the proposed AMRU++ network for medical image segmentation (b) multi-scale residual (MR) block.

Figure 3

Examples of augmented images (b–f) generated from a normal image (a) using various data augmentation techniques. (b) Rotation, (c) Flip, (d) Scale, (e) Cutout, and (f) Scutout (proposed).

Figure 4

Bubble plots showing statistical significance based on Mann-Whitney U test on each pair of networks in terms of DSC on three datasets. ‘no significance’ indicates not statistically significant, bubbles with different colours and sizes indicate different levels of significance calculated by p-values.

Figure 5

Comparison of segmentation performance of the U-Net model using DSC with different augmentation techniques and different percentages (%) of augmented images on three datasets. ‘no_aug’ indicates no augmentation, and Conv stands for conventional data augmentation technique.

Figure 6

Visual comparison of segmentation performance for models w/o DA and with PDA. One image was selected from each of the three datasets. For CXR images, two predicted masks are shown, one without any data augmentation denoted as ‘w/o DA’ and another with the proposed data augmentation denoted as ‘with PDA’.