Pleural nodule identification in low-dose and thin-slice lung computed tomography

Authors:
A. Retico;M. E. Fantacci;I. Gori;P. Kasae;B. Golosio;A. Piccioli;P. Cerello;G. De Nunzio;S. Tangaro
Affiliations:
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy and Dipartimento di Fisica, Universití di Pisa, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy and Bracco Imaging S.p.A., Milano, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Italy and Dipartimento di Matematica e Fisica, Universití di Sassari, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Italy and Dipartimento di Matematica e Fisica, Universití di Sassari, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Italy;Dipartimento di Scienza dei Materiali, Universití del Salento, Italy and Istituto Nazionale di Fisica Nucleare, Sezione di Lecce, Italy;Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
Venue:
Computers in Biology and Medicine
Year:
2009

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Abstract

A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by 12 morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with six FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).