Multi-path Aggregation U-Net for Lung Segmentation in Chest Radiographs

Abstract

Lung segmentation from chest X-ray images is a fundamental and crucial step for computer-aid diagnosis (CAD) system. Although many techniques for this problem have been proposed, it still remains as a challenge. Recently, Fully Convolutional Networks (FCNs) especially U-Net has been hugely successful for many image segmentation tasks. In this paper, we propose a revised variant of U-Net, specifically, we design two main components. The first component is multi-path dilated convolutions with different dilation rate to extract multi-scale features. It was used to replace the basic convolutions used in the original U-Net. The second component is skip connections with dense deep layer aggregation to further aggregate features across different scales. We perform extensive experiments on three publicly available datasets (in total 951 images). Our proposed method outperforms many other segmentation methods and achieves state-of-the-art segmentation performance (Dice’s coefficient of 96.5%, 97.9% and 96.7% on the three datasets, respectively).

TransCotANet: A Lung Field Image Segmentation Network with Multidimensional Global Feature Dynamic Aggregation

“…We evaluated TransCotANet with two different types of methods. Three U-Net-based methods were included: U-Net [7], MPDC DDLA U-Net [40], and MultiResUNet [41]; along with four other network-based methods: Deeplabv3 [29], AG-net [42], CNN+Neural Net [43], and LF-Net [34]; and one state-of-the-art Transformer-based segmentation method: UCTransNet [15]. The results of our experiments on the Shenzhen dataset are reported in Table 4, where the best results are shown in bold.…”