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BMC Medical Informatics and Decision Making

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Open Access 01-12-2020 | Pneumothorax | Research

Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

Authors: Qingfeng Wang, Qiyu Liu, Guoting Luo, Zhiqin Liu, Jun Huang, Yuwei Zhou, Ying Zhou, Weiyun Xu, Jie-Zhi Cheng

Published in: BMC Medical Informatics and Decision Making | Special Issue 14/2020

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 boundary 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 \(0.93\pm 0.13\) and dice similarity coefficient (DSC) with \(0.92\pm 0.14\), and achieves competitive performance on diagnostic accuracy with 93.45% and \(F_1\)-score with 92.97%.

Conclusion

This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

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Title: Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study
Authors: Qingfeng Wang
Qiyu Liu
Guoting Luo
Zhiqin Liu
Jun Huang
Yuwei Zhou
Ying Zhou
Weiyun Xu
Jie-Zhi Cheng
Publication date: 01-12-2020
Publisher: BioMed Central
Keywords: Pneumothorax
Pneumothorax
Pneumothorax
Published in: BMC Medical Informatics and Decision Making / Issue Special Issue 14/2020
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-020-01325-5

At a glance: The STEP trials

Springer Medicine

Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

Abstract

Background

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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