Retico, Alessandra and Fantacci, Maria Evelina and Gori, Ilaria and Kasae, Parnian and Golosio, Bruno and Piccioli, Alessio and Cerello, Piergiorgio and De Nunzio, Giorgio and Tangaro, Sonia (2009) Pleural nodule identification in low-dose and thin-slice lung computed tomography. Computers in Biology and Medicine, Vol. 39 (12), p. 1127-1144. eISSN 1879-0534. Article.
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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).
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