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ALTIS: A fast and automatic lung and trachea CT‐image segmentation method
Author(s) -
Sousa Azael M.,
Martins Samuel B.,
Falcão Alexandre X.,
Reis Fabiano,
Bagatin Ericson,
Irion Klaus
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13773
Subject(s) - robustness (evolution) , segmentation , lung , image segmentation , artificial intelligence , sørensen–dice coefficient , computed tomography , medical imaging , computer science , medicine , nuclear medicine , pattern recognition (psychology) , radiology , computer vision , biology , biochemistry , gene
The automated segmentation of each lung and trachea in CT scans is commonly taken as a solved problem. Indeed, existing approaches may easily fail in the presence of some abnormalities caused by a disease, trauma, or previous surgery. For robustness, we present ALTIS (implementation is available at http://lids.ic.unicamp.br/downloads) - a fast automatic lung and trachea CT-image segmentation method that relies on image features and relative shape- and intensity-based characteristics less affected by most appearance variations of abnormal lungs and trachea.