title: A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography.
abstract: The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis, carotid stiffness, coronary artery calcification, atrial fibrillation and many others. These deposits vary unrelated to obesity, which reinforces its direct segmentation for further quantification. However, manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians. In this work, we propose a unified method for an autonomous segmentation and quantification of two types of cardiac fats. The segmented fats are termed epicardial and mediastinal, and stand apart from each other by the pericardium. Much effort was devoted to achieve minimal user intervention. The proposed methodology mainly comprises registration and classification algorithms to perform the desired segmentation. We compare the performance of several classification algorithms on this task, including neural networks, probabilistic models and decision tree algorithms. Experimental results of the proposed methodology have shown that the mean accuracy regarding both epicardial and mediastinal fats is 98.5% (99.5% if the features are normalized), with a mean true positive rate of 98.0%. In average, the Dice similarity index was equal to 97.6%.
authors.affiliation: Department of Computer Science, Universidade Federal Fluminense (UFF), Rua Passo da Pátria 156, Niterói, Rio de Janeiro, Brazil. Electronic address: erickr@id.uff.br.
authors.forename: É O
authors.initials: ÉO
authors.lastname: Rodrigues
authors.affiliation: Department of Internal Medicine and Endocrine Unit, Medical School and Hospital Universitário Clementino Fraga Filho, Universidade Federal do Rio de Janeiro (UFRJ), Rua Rodolpho Paulo Rocco, 255 - Cidade Universitária, Rio de Janeiro, Brazil.
authors.forename: F F C
authors.initials: FF
authors.lastname: Morais
authors.affiliation: Department of Internal Medicine and Endocrine Unit, Medical School and Hospital Universitário Clementino Fraga Filho, Universidade Federal do Rio de Janeiro (UFRJ), Rua Rodolpho Paulo Rocco, 255 - Cidade Universitária, Rio de Janeiro, Brazil.
authors.forename: N A O S
authors.initials: NA
authors.lastname: Morais
authors.affiliation: Department of Specialized Medicine, Universidade Federal do Espírito Santo (UFES), Av. Marechal Campos, 1468 - Maruípe, Vitória, Brazil.
authors.forename: L S
authors.initials: LS
authors.lastname: Conci
authors.affiliation: Department of Internal Medicine and Endocrine Unit, Medical School and Hospital Universitário Clementino Fraga Filho, Universidade Federal do Rio de Janeiro (UFRJ), Rua Rodolpho Paulo Rocco, 255 - Cidade Universitária, Rio de Janeiro, Brazil.
authors.forename: L V
authors.initials: LV
authors.lastname: Neto
authors.affiliation: Department of Computer Science, Universidade Federal Fluminense (UFF), Rua Passo da Pátria 156, Niterói, Rio de Janeiro, Brazil.
authors.forename: A
authors.initials: A
authors.lastname: Conci
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D000465:Algorithms; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods / Q000706:statistics & numerical data; D008482:Mediastinum / Q000000981:diagnostic imaging; D008490:Medical Informatics; D010496:Pericardium / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data
keywords: Atlas; Classification; Computed tomography; Image registration; Machine learning; Segmentation