title: A novel multithreshold method for nodule detection in lung CT.
abstract: Multislice computed tomography (MSCT) is a valuable tool for lung cancer detection, thanks to its ability to identify noncalcified nodules of small size (from about 3 mm). Due to the large number of images generated by MSCT, there is much interest in developing computer-aided detection (CAD) systems that could assist radiologists in the lung nodule detection task. A complete multistage CAD system, including lung boundary segmentation, regions of interest (ROIs) selection, feature extraction, and false positive reduction is presented. The selection of ROIs is based on a multithreshold surface-triangulation approach. Surface triangulation is performed at different threshold values, varying from a minimum to a maximum value in a wide range. At a given threshold value, a ROI is defined as the volume inside a connected component of the triangulated isosurface. The evolution of a ROI as a function of the threshold can be represented by a treelike structure. A multithreshold ROI is defined as a path on this tree, which starts from a terminal ROI and ends on the root ROI. For each ROI, the volume, surface area, roundness, density, and moments of inertia are computed as functions of the threshold and used as input to a classification system based on artificial neural networks. The method is suitable to detect different types of nodules, including juxta-pleural nodules and nodules connected to blood vessels. A training set of 109 low-dose MSCT scans made available by the Pisa center of the Italung-CT trial and annotated by expert radiologists was used for the algorithm design and optimization. The system performance was tested on an independent set of 23 low-dose MSCT scans coming from the Pisa Italung-CT center and on 83 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. On the Italung-CT test set, for nodules having a diameter greater than or equal to 3 mm, the system achieved 84% and 71% sensitivity at false positive/scan rates of 10 and 4, respectively. For nodules having a diameter greater than or equal to 4 mm, the sensitivities were 97% and 80% at false positive/scan rates of 10 and 4, respectively. On the LIDC data set, the system achieved a 79% sensitivity at a false positive/scan rate of 4 in the detection of nodules with a diameter greater than or equal to 3 mm that have been annotated by all four radiologists.
authors.affiliation: Struttura Dipartimentale di Matematica e Fisica, Università di Sassari, via Vienna 2, 07100 Sassari, Italy. golosio@uniss.it
authors.forename: Bruno
authors.initials: B
authors.lastname: Golosio
authors.forename: Giovanni Luca
authors.initials: GL
authors.lastname: Masala
authors.forename: Alessio
authors.initials: A
authors.lastname: Piccioli
authors.forename: Piernicola
authors.initials: P
authors.lastname: Oliva
authors.forename: Massimo
authors.initials: M
authors.lastname: Carpinelli
authors.forename: Rosella
authors.initials: R
authors.lastname: Cataldo
authors.forename: Piergiorgio
authors.initials: P
authors.lastname: Cerello
authors.forename: Francesco
authors.initials: F
authors.lastname: De Carlo
authors.forename: Fabio
authors.initials: F
authors.lastname: Falaschi
authors.forename: Maria Evelina
authors.initials: ME
authors.lastname: Fantacci
authors.forename: Gianfranco
authors.initials: G
authors.lastname: Gargano
authors.forename: Parnian
authors.initials: P
authors.lastname: Kasae
authors.forename: Massimo
authors.initials: M
authors.lastname: Torsello
mesh_terms: D005189:False Positive Reactions; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008954:Models, Biological; D016571:Neural Networks, Computer; D035441:Thoracic Wall / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*