Pleural nodule identification in low-dose and thin-slice lung computed tomography.
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).
Retico
A
A
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy. alessandra.retico@pi.infn.it
Fantacci
M E
ME
Gori
I
I
Kasae
P
P
Golosio
B
B
Piccioli
A
A
Cerello
P
P
De Nunzio
G
G
Tangaro
S
S
D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D005189:False Positive Reactions; D006801:Humans; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data