H-SegNet: Hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method

Tao Peng, Caishan Wang, You Zhang, Jing Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Chest x-ray (CXR) is one of the most commonly used imaging techniques for the detection and diagnosis of pulmonary diseases. One critical component in many computer-aided systems, for either detection or diagnosis in digital CXR, is the accurate segmentation of the lung. Due to low-intensity contrast around lung boundary and large inter-subject variance, it has been challenging to segment lung from structural CXR images accurately. In this work, we propose an automatic Hybrid Segmentation Network (H-SegNet) for lung segmentation on CXR. The proposed H-SegNet consists of two key steps: (1) an image preprocessing step based on a deep learning model to automatically extract coarse lung contours; (2) a refinement step to fine-tune the coarse segmentation results based on an improved principal curve-based method coupled with an improved machine learning method. Experimental results on several public datasets show that the proposed method achieves superior segmentation results in lung CXRs, compared with several state-of-the-art methods.

Original languageEnglish (US)
Article number075006
JournalPhysics in medicine and biology
Volume67
Issue number7
DOIs
StatePublished - Apr 7 2022
Externally publishedYes

Keywords

  • adaptive closed polyline searching method
  • adaptive memory-based differential evolution method
  • automatic lung segmentation
  • chest radiographs
  • mask-RCNN
  • principal curve

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'H-SegNet: Hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method'. Together they form a unique fingerprint.

Cite this