A-LugSeg: Automatic and explainability-guided multi-site lung detection in chest X-ray images

•A cascade framework is proposed for automatic lung segmentation in CXRs.•An adaptive closed polyline searching method is used to obtain data sequence.•An improved machine learning model is proposed to express a mathematical model.•The explainability-guided mathematical model is used to denote lung...

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Veröffentlicht in:Expert systems with applications Jg. 198; S. 116873
Hauptverfasser: Peng, Tao, Gu, Yidong, Ye, Zhenyu, Cheng, Xiuxiu, Wang, Jing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.07.2022
Elsevier BV
Schlagworte:
Algorithms
Automatic lung segmentation
Chest
Chest radiographs
Datasets
Deep learning
Explainability-guided mathematical model
Fractional-order backpropagation learning algorithm
Image segmentation
Improved adaptive closed polyline searching algorithm
Lungs
Machine learning
Mask-RCNN
Mathematical models
Multi-site dataset
Performance evaluation
Principal curve
Sensitivity
Similarity coefficient method
X-rays
Improved adaptive closed polyline searching algorithm
Multi-site dataset
Mask-RCNN
Chest radiographs
Fractional-order backpropagation learning algorithm
Automatic lung segmentation
Principal curve
Explainability-guided mathematical model
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•A cascade framework is proposed for automatic lung segmentation in CXRs.•An adaptive closed polyline searching method is used to obtain data sequence.•An improved machine learning model is proposed to express a mathematical model.•The explainability-guided mathematical model is used to denote lung contour.•Performance of the proposed method is evaluated on public multi-site datasets. Large variations in anatomical shape and size, too much overlap between anatomical structures, and inconsistent anatomical shapes make accurate lung segmentation in chest x-rays (CXR) a challenging problem. In this paper, we propose an automatic method called A-LugSeg that consists of two subnetworks for lung segmentation in CXRs. The first is a segmentation subnetwork based on a deep learning model (i.e., Mask-RCNN), which completes a coarse segmentation for each input CXR image. The second is a refinement subnetwork designed to optimize the coarse segmentation result by combining an improved closed principal curve method and an enhanced machine learning, where the lung contour’s explainability-guided mathematical model is expressed by the machine learning’s parameters. The performance of the proposed method is evaluated on three public datasets, namely the ShenZhen hospital Chest X-ray dataset (SZCX), Japanese Society of Radiological Technology dataset (JSRT), and Montgomery County chest x-ray dataset (MC), which contain the 662 CXRs, 247 CXRs, and 138 CXRs, respectively. We used different datasets for training/validation (SZCX) and testing (SZCX/JSRT/MC). Furthermore, we used six evaluation metrics to evaluate the performance of our proposed method, including Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), Accuracy (ACC), Precision, Sensitivity, and Specificity. The obtained results (DSC = 0.973, Ω = 0.958, ACC = 0.972, and p-value for DSC < 0.001) for JSRT, (DSC = 0.971, Ω = 0.955, ACC = 0.97, and p-value for DSC < 0.001) for MC, (DSC = 0.972, Ω = 0.956, and ACC = 0.97) for hybrid datasets (JSRT + MC), and (Precision, Sensitivity, and Specificity are higher than 0.98) show the superior performance of the proposed dual subnetwork segmentation algorithm compared to the existing state of the art approaches.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.116873