dp-BREATH: Heat maps and probabilistic classification assisting the analysis of abnormal lung regions.
Algorithms
Computer Simulation
Diagnosis, Computer-Assisted
/ methods
Humans
Likelihood Functions
Lung
/ abnormalities
Models, Statistical
Normal Distribution
Pattern Recognition, Automated
/ methods
Principal Component Analysis
Probability
Radiology
/ methods
Reproducibility of Results
Tomography, X-Ray Computed
User-Computer Interface
CBIR
Heat map representation
Lung classification
Statistical modeling
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
May 2019
May 2019
Historique:
received:
05
07
2018
revised:
15
01
2019
accepted:
21
01
2019
entrez:
4
5
2019
pubmed:
3
5
2019
medline:
26
11
2019
Statut:
ppublish
Résumé
Identifying abnormalities in chest CT scans is an important and challenging task, demanding time and effort from specialists. Different parts of a single lung image may present both normal and abnormal characteristics. Thus, detecting a single lung as healthy (normal) or not is inaccurate. In this work we propose dp-BREATH, a method capable of detecting abnormalities in pulmonary tissue regions and directing the specialist's attention to the lung region containing them. It starts by highlighting regions that may indicate pulmonary abnormalities based on the healthy pulmonary tissue behavior using a superpixel-based approach and a heat map visualization. This is achieved by modeling regions of healthy tissue using a statistical model. All regions considered abnormal are modeled and classified according to their probability of containing each of the studied abnormalities. Further, dp-BREATH provides a better recognition of radiological patterns, with the likelihood of a selected lung region to contain abnormalities. We validate the statistical model of healthy and abnormal detection using a representative dataset of chest CT scans. The model has shown almost no overlap between healthy and abnormal regions, and the detection of abnormalities presented precision higher than 86%, for all recall values. Additionally, the fitted models describing pulmonary radiological patterns present precision of up to 87%, with a high separation for three of five radiological patterns. dp-BREATH's heat map representation and its list of radiological patterns probabilities provided are intuitive methods to assist physicians during diagnosis.
Sections du résumé
BACKGROUND AND OBJECTIVE
OBJECTIVE
Identifying abnormalities in chest CT scans is an important and challenging task, demanding time and effort from specialists. Different parts of a single lung image may present both normal and abnormal characteristics. Thus, detecting a single lung as healthy (normal) or not is inaccurate.
METHODS
METHODS
In this work we propose dp-BREATH, a method capable of detecting abnormalities in pulmonary tissue regions and directing the specialist's attention to the lung region containing them. It starts by highlighting regions that may indicate pulmonary abnormalities based on the healthy pulmonary tissue behavior using a superpixel-based approach and a heat map visualization. This is achieved by modeling regions of healthy tissue using a statistical model. All regions considered abnormal are modeled and classified according to their probability of containing each of the studied abnormalities. Further, dp-BREATH provides a better recognition of radiological patterns, with the likelihood of a selected lung region to contain abnormalities.
RESULTS
RESULTS
We validate the statistical model of healthy and abnormal detection using a representative dataset of chest CT scans. The model has shown almost no overlap between healthy and abnormal regions, and the detection of abnormalities presented precision higher than 86%, for all recall values. Additionally, the fitted models describing pulmonary radiological patterns present precision of up to 87%, with a high separation for three of five radiological patterns.
CONCLUSIONS
CONCLUSIONS
dp-BREATH's heat map representation and its list of radiological patterns probabilities provided are intuitive methods to assist physicians during diagnosis.
Identifiants
pubmed: 31046993
pii: S0169-2607(18)31103-9
doi: 10.1016/j.cmpb.2019.01.014
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
27-34Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.