title: Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.
abstract: In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: francesco.ciompi@radboudumc.nl.
authors.forename: Francesco
authors.initials: F
authors.lastname: Ciompi
authors.affiliation: University Medical Center, Utrecht, The Netherlands.
authors.forename: Bartjan
authors.initials: B
authors.lastname: de Hoop
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: van Riel
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Kaman
authors.initials: K
authors.lastname: Chung
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Ernst Th
authors.initials: ET
authors.lastname: Scholten
authors.affiliation: University Medical Center, Groningen, The Netherlands.
authors.forename: Matthijs
authors.initials: M
authors.lastname: Oudkerk
authors.affiliation: University Medical Center, Utrecht, The Netherlands.
authors.forename: Pim A
authors.initials: PA
authors.lastname: de Jong
authors.affiliation: Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Mevis, Bremen, Germany.
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Chest CT; Convolutional neural networks; Deep learning; Lung cancer screening; OverFeat; Peri-fissural nodules