TY - GEN
T1 - Accuracy improvement of lung cancer detection based on spatial statistical analysis of thoracic CT scans
AU - Takizawa, Hotaka
AU - Yamamoto, Shinji
AU - Shiina, Tsuyoshi
PY - 2007
Y1 - 2007
N2 - This paper describes a novel discrimination method of lung cancers based on statistical analysis of thoracic computed tomography (CT) scans. Our previous Computer-Aided Diagnosis (CAD) system can detect lung cancers from CT scans, but, at the same time, yields many false positives. In order to reduce the false positives, the method proposed in the present paper uses a relationship between lung cancers, false positives and image information on CT scans. The trend of variation of the relationships is acquired through statistical analysis of a set of CT scans prepared for training. In testing, by use of the trend, the method predicts the appearance of lung cancers and false positives in a CT scan, and improves the accuracy of the previous CAD system by modifying the system's output based on the prediction. The method is applied to 218 actual thoracic CT scans with 386 actual lung cancers. Receiver operating characteristic (ROC) analysis is used to evaluate the results. The area under the ROC curve (Az) is statistically significantly improved from 0.918 to 0.931.
AB - This paper describes a novel discrimination method of lung cancers based on statistical analysis of thoracic computed tomography (CT) scans. Our previous Computer-Aided Diagnosis (CAD) system can detect lung cancers from CT scans, but, at the same time, yields many false positives. In order to reduce the false positives, the method proposed in the present paper uses a relationship between lung cancers, false positives and image information on CT scans. The trend of variation of the relationships is acquired through statistical analysis of a set of CT scans prepared for training. In testing, by use of the trend, the method predicts the appearance of lung cancers and false positives in a CT scan, and improves the accuracy of the previous CAD system by modifying the system's output based on the prediction. The method is applied to 218 actual thoracic CT scans with 386 actual lung cancers. Receiver operating characteristic (ROC) analysis is used to evaluate the results. The area under the ROC curve (Az) is statistically significantly improved from 0.918 to 0.931.
KW - Computer-aided diagnosis
KW - Detection of lung cancers
KW - Spatial relationship
KW - Statistical analysis
KW - Thoracic CT scans
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U2 - 10.1007/978-3-540-71457-6_56
DO - 10.1007/978-3-540-71457-6_56
M3 - Conference contribution
AN - SCOPUS:37149026075
SN - 3540714561
SN - 9783540714569
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 607
EP - 617
BT - Computer Vision/Computer Graphics Collaboration Techniques - Third International Conference, MIRAGE 2007, Proceedings
PB - Springer Verlag
T2 - 3rd International Conference, MIRAGE 2007: Computer Vision/Computer Graphics Collaboration Techniques
Y2 - 28 March 2007 through 30 March 2007
ER -