Combination of computer-aided detection algorithms for automatic lung nodule identification

PurposeThe aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans.MethodsThe outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were combined. The systems are: the CAMCADe (based on a Channeler-Ant-Model which segments vessel tree and nodule candidates and a neural classifier), the RGVPCADe (a Region-Growing- Volume-Plateau algorithm detects nodule candidates and a neural network reduces false positives); the VBNACADe (two dedicated procedures, based respectively on a 3D dot-enhancement algorithm and on intersections of pleura surface normals, identifies internal and juxtapleural nodules, and a Voxel-Based-Neural-Approach reduces false positives. A dedicated OsiriX plugin implemented with the Cocoa environments of MacOSX allows annotating nodules and visualizing singles and combined CADe findings.ResultsThe combined CADe has been tested on thin slice (lower than 2 mm) CTs of the LIDC public research database and the results have been compared with those obtained by the single systems. The FROC (Free Receiver Operating Characteristic) curves show better results than the best of the single approaches.ConclusionsHas been demonstrated that the combination of different approaches offers better results than each single CADe system. A clinical validation of the combined CADe as second reader is being addressed by means of the dedicated OsiriX plugin.

[1]  Piergiorgio Cerello,et al.  A novel multithreshold method for nodule detection in lung CT. , 2009, Medical physics.

[2]  Sabina Sonia Tangaro,et al.  Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar Region , 2011, Journal of Digital Imaging.

[3]  Marco Das,et al.  Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. , 2006, Radiology.

[4]  Ilaria Gori,et al.  Pleural nodule identification in low-dose and thin-slice lung computed tomography , 2009, Comput. Biol. Medicine.

[5]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[6]  Roberto Bellotti,et al.  3-D object segmentation using ant colonies , 2010, Pattern Recognit..

[7]  Qiang Li,et al.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. , 2003, Medical physics.

[8]  S. Matsumoto,et al.  Computer-aided detection of lung nodules on multidetector row computed tomography using three-dimensional analysis of nodule candidates and their surroundings , 2008, Radiation Medicine.

[9]  C Beigelman-Aubry,et al.  thorax - Évaluation de l’impact d’un système CAD sur la performance des radiologues pour la détection des nodules pulmonaires sur des examens scanographiques multicoupes du thorax , 2007 .

[10]  Narinder Paul,et al.  The utility of computer-aided detection (CAD) for lung cancer screening using low-dose CT , 2005 .

[11]  Joyoni Dey,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[12]  Ilaria Gori,et al.  Lung nodule detection in low-dose and thin-slice computed tomography , 2008, Comput. Biol. Medicine.

[13]  Ilaria Gori,et al.  A voxel-based neural approach (VBNA) to identify lung nodules in the ANODE09 study , 2009, Medical Imaging.

[14]  Roberto Bellotti,et al.  The Channeler Ant Model: Object segmentation with virtual ant colonies , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[15]  Ilaria Gori,et al.  Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study , 2010, Medical Image Anal..

[16]  B Brochu,et al.  [Computer-aided detection of lung nodules on thin collimation MDCT: impact on radiologists' performance]. , 2007, Journal de radiologie.

[17]  Max A. Viergever,et al.  On Combining Computer-Aided Detection Systems , 2011, IEEE Transactions on Medical Imaging.

[18]  R. Bellotti,et al.  A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. , 2007, Medical physics.

[19]  Zaid J. Towfic,et al.  The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation , 2007, SPIE Medical Imaging.

[20]  C. Mathers,et al.  Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008 , 2010, International journal of cancer.

[21]  Piergiorgio Cerello,et al.  Computer aided detection of nodules in low dose and thin slice lung CT , 2010 .

[22]  O. Miettinen,et al.  Survival of Patients with Stage I Lung Cancer Detected on CT Screening , 2008 .

[23]  Vitoantonio Bevilacqua,et al.  Distributed medical images analysis on a Grid infrastructure , 2007, Future Gener. Comput. Syst..

[24]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[25]  W. Heindel,et al.  Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting , 2001, European Radiology.