About: Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels

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About: Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels
Creator	Liu, Caixia Pang, Mingyong Xie, Wangli Zhao, Ruibin
Source	Medline; PMC
abstract	Accurate segmentation of lungs in pathological thoracic computed tomography (CT) scans plays an important role in pulmonary disease diagnosis. However, it is still a challenging task due to the variability of pathological lung appearances and shapes. In this paper, we proposed a novel segmentation algorithm based on random forest (RF), deep convolutional network, and multi-scale superpixels for segmenting pathological lungs from thoracic CT images accurately. A pathological thoracic CT image is first segmented based on multi-scale superpixels, and deep features, texture, and intensity features extracted from superpixels are taken as inputs of a group of RF classifiers. With the fusion of classification results of RFs by a fractional-order gray correlation approach, we capture an initial segmentation of pathological lungs. We finally utilize a divide-and-conquer strategy to deal with segmentation refinement combining contour correction of left lungs and region repairing of right lungs. Our algorithm is tested on a group of thoracic CT images affected with interstitial lung diseases. Experiments show that our algorithm can achieve a high segmentation accuracy with an average DSC of 96.45% and PPV of 95.07%. Compared with several existing lung segmentation methods, our algorithm exhibits a robust performance on pathological lung segmentation. Our algorithm can be employed reliably for lung field segmentation of pathologic thoracic CT images with a high accuracy, which is helpful to assist radiologists to detect the presence of pulmonary diseases and quantify its shape and size in regular clinical practices.
has issue date	2020-08-07(xsd:dateTime)
bibo:doi	10.1007/s11063-020-10330-8
bibo:pmid	32837245
has license	no-cc
sha1sum (hex)	c78c04b491986bb79c46cc9cf38f561d2a9cf7a9
schema:url	https://doi.org/10.1007/s11063-020-10330-8
resource representing a document's title	Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels
has PubMed Central identifier	PMC7413019
has PubMed identifier	32837245
schema:publication	Neural Process Lett
resource representing a document's body	covid:c78c04b491986bb79c46cc9cf38f561d2a9cf7a9#body_text
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