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

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Automatic lung segmentation,Chest radiographs,Multi-site dataset,Mask-RCNN,Principal curve,Improved adaptive closed polyline searching algorithm,Fractional-order backpropagation learning algorithm,Explainability-guided mathematical model

论文评审过程：Received 3 May 2021, Revised 15 September 2021, Accepted 9 March 2022, Available online 15 March 2022, Version of Record 18 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116873