Ivan De Mitri * on behalf of MAGIC-5 collaboration *Dipartimento di Fisica dell’Università del Salento and INFN, Lecce, Italy 12th.

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Presentation transcript:

Ivan De Mitri * on behalf of MAGIC-5 collaboration *Dipartimento di Fisica dell’Università del Salento and INFN, Lecce, Italy 12th International Conference on Applied Stochastic Models and Data Analysis Chania, Crete, Greece, May 29- June 1, 2007 Implementing Computer Assisted Detectionn systems for the analysis of mammograms, lung CT scans, and brain PET and NMR images

The Magic-5 Project 2 Ivan De Mitri The medical applications of the MAGIC-5 project cover at present three main fields: 1. breast cancer detection in mammograms 2. nodule detection in lung CT images 3. the diagnosis of the Alzheimer disease (AD) …by using also the GRID ! MAGIC–5 Medical Applications on a Grid Infrastructure Connection

The Magic-5 Project 3 Ivan De Mitri MAGIC–5 Medical Applications on a Grid Infrastructure Connection A collaboration of several Universities, Local INFN Section and Hospitals International Collaborations Centro de Applicaciones Tecnologicas y Desarrollo Nuclear (CEADEN), Cuba ALICE collaboration – CERN Ginevra Collaborations with Industries BRACCO Imaging, EURIX, I&T

The Magic-5 Project 4 Ivan De Mitri CAD Station for Mammography Massive Lesion Microcalcifications Image Selection Image manipulation Metadata insertion Diagnosis insertion CAD execution Data Registration Data Search Installations Hospitals: Valdese (TO) Palermo Lecce INFN-Universities: Bari, Lecce, Napoli, Palermo,Torino, Sassari

The Magic-5 Project 5 Ivan De Mitri CircularityInertial Momentum Mean Radial LengthMean Intensity STD of theRadial LengthSTD of the Intensity Entropy of the intensity distribution Anisotropy Fractal indexArea Eccentricity………………………… CAD for mammography: Some of the used features

The Magic-5 Project 6 Ivan De Mitri

The Magic-5 Project 7 Ivan De Mitri Density Comparison A Code for the scale normalisation was developed based on the overlap of the area outside the breast Before TreatmentAfter Treatment Density measurement at different times will allow the patient monitoring during different types of therapy Breast range starts here

The Magic-5 Project 8 Ivan De Mitri Nodule detection in lung CT scans Two steps already implemented 1. automated extraction of the pulmonary parenchyma; 2. detection of nodule candidates based on several independent methods

The Nodule Topology n 1:internal n 3:pleural n 2:sub-pleural

The Magic-5 Project 10 Ivan De Mitri First Threshold identification (Intensity histogram on a central slice) 3D Region Growing 3D Airways segmentation Cranio-caudal Sorting of images in the dicomdir … Lung CAD: One of the approaches

The Magic-5 Project 11 Ivan De Mitri Wavefront algorithm Bronchial tree segmentation Threshold adjusting (avoid lungs fusions, etc.) Authomatic trachea identification …

The Magic-5 Project 12 Ivan De Mitri ROI Hunter 3D False positive filtering … slice z slice z+1 slice z-1

The Magic-5 Project 13 Ivan De Mitri

The Magic-5 Project 14 Ivan De Mitri

The Magic-5 Project 15 Ivan De Mitri

The Magic-5 Project 16 Ivan De Mitri Results from CAD for lung CT (for one of the implemented algorithms) 4. M.S. Brown, J.G. Goldin, S. Rogers, H.J. Kim, R.D. Suh, M.F. McNitt-Gray, S.K. Shah, D. Truong, K. Brown, J.W. Sayre, D.W. Gjertson, P. Batra, and D.R. Aberle, “Computer-aided Lung Nodule Detection in CT: Results of Large- Scale Observer Test”, Academic Radiology 12 (6), (2005). 6.K. Suzuki, S.G. Armato III, F. Li, S. Sone, and K. Doi, “Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography”, Medical Physics 30 (7), (2003). 7.M.N. Gurcan, B. Sahiner, N. Petrick, H.-P. Chan, E.A. Kazerooni, P.N. Cas- cade, and L. Hadjiiski, “Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system”, Medical Physics 29 (11), (2002). 8.Y. Lee, T. Hara, H. Fujita, S. Itoh, and T. Ishigaki, “Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template- Matching Technique”, IEEE Transaction on Medical Imaging, Vol. 20, No. 7, (2001). 9.9 A.S. Roy, S.G. Armato III, A. Wilson, K. and Drukker, “Automated detection of lung nodules in ct scans: False positives reduction with the radial- gradient index”, Medical Physics 33 (4), (2006).

The Magic-5 Project 17 Ivan De Mitri The case of the Alzheimer desease The quantitative comparison, through the SPM (Statistical Parametric Mapping) software, of PET images from suspected AD patients with images of “normal” cases, allows powerful suggestions to an early AD diagnosis. The use of an integrated GRID environment for the remote and distributed processing of the PET images at a large scale, is strongly desirable. This application is implemented in the MAGIC-5 GRID infrastructure.

The Magic-5 Project 18 Ivan De Mitri Use both NMR and PET images

The Magic-5 Project 19 Ivan De Mitri Rate of Atrophy (mm 3 /yr) Controls AD NormalAD ROC Area 93% First results are encouraging

The Magic-5 Project 20 Ivan De Mitri Summary CAD for mammography Several working prototypes are being installed and tested in different accademic sites and hospitals Upcoming participations to real screening programs CAD for lung CT scans Different approaches gave promising results in terms of both sensitivity and false positive fraction Upcoming test on large scale databases Early diagnosis of AD Good preliminary results obtained from the hippocampus segmentation Tests are under way to combine information from different diagnosis tools (NMR, PET,..)