Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet:
- PDF / 1,814,099 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 44 Downloads / 159 Views
Open Access
RESEARCH
Automated segmentation and diagnosis of pneumothorax on chest X‑rays with fully convolutional multi‑scale ScSE‑DenseNet: a retrospective study Qingfeng Wang1†, Qiyu Liu2†, Guoting Luo1, Zhiqin Liu1*, Jun Huang1, Yuwei Zhou1, Ying Zhou2, Weiyun Xu2 and Jie‑Zhi Cheng3
From 10th International Workshop on Biomedical and Health Informatics San Diego, CA, USA. 18-20 November 2019
Abstract Background: Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. Methods: In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of bound‑ ary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. Results: This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with 0.93 ± 0.13 and dice similarity coefficient (DSC) with 0.92 ± 0.14 , and achieves competitive performance on diagnostic accuracy with 93.45% and F1-score with 92.97%.
*Correspondence: [email protected] † Qingfeng Wang and Qiyu Liu have contributed equally 1 School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China Full list of author information is available at the end of the article © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permis
Data Loading...
