H-SegNet: hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method.
Phys Med Biol
; 67(7)2022 03 29.
Article
in English
| MEDLINE | ID: covidwho-1774310
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Lung Diseases
Type of study:
Diagnostic study
Limits:
Humans
Language:
English
Year:
2022
Document Type:
Article
Affiliation country:
1361-6560
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