L'analisi delle immagini mediche per la diagnosi precoce delle neoplasie Roberto BELLOTTI Dipartimento Interateneo di Fisica M. Merlin Università degli Studi di Bari e Politecnico di Bari Istituto Nazionale di Fisica Nucleare - Sezione di Bari on behalf of the MAGIC-5 Collaboration

Main research activities Developing models and algorithms for a distributed analysis of biomedical images:  to support the radiologist's diagnosis with Computer-Aided Detection (CAD) algorithms  to improve computational speed, data accessibility and sharing of distributed image databases  to enable the co-working of medical experts and to allow large-scale statistical and epidemiological analysis Analysis of Medical Images:  Mammographic images for the early diagnosis of breast cancer (>2002)  Computed Tomography images for the early diagnosis of lung cancer (>2004)  MRI of the brain for the early diagnosis of the Alzheimer’s disease (>2006)

Interdisciplinary know-how Several techniques developed in High Energy Physics (HEP) and astrophysics experiments are implemented and optimized in medical image analysis to detect very low signal in a noisy background O. Adriani et al. (PAMELA Collaboration) Observation of an anomalous positron abundance in the cosmic radiation, NATURE, in print (2009)

Computer-aided detection of breast lesions

Image Acquisition & Manipulation (DICOM) Metadata & Diagnosis insertion CAD execution Data storage & retrieve through the GRID Operating Installations: Torino (Valdese), Lecce, Bari Hospitals Suzanne Mubarak Centre for Women's Health and Development, Alexandria (EGY) MASSES MICROCALCIFICATION CLUSTERS CAD station for Mammography Massive Lesion Microcalcifications [P. Cerello et al, Methods Inf Med 44, (2005)]

Lesion Massives Recognition Segmentation: ROI (Region Of Interest) hunter Feature Extraction : co-occurrece matrix Classification: Neural Network

Breast CAD Area:0.783

Computer-aided detection of lung nodules in screening CT

Non-calcified small pulmonary nodules are considered as the primary signs of early-stage lung cancers Nodules with diameter ≥ 5mm have to be detected A CAD system could be useful as first or second reader It should be characterized by:  high sensitivity  low number of false-positive findings (FPs) per scan Lung CT Screening and Computer- Aided Detection (CAD) Thin-slice CT: Reconstructed slice thickness =1mm → ~300 slices/scan Low-dose helical multi-slice CT 0.6 mSv Low-dose helical single-slice CT 1.2 mSv Standard dose helical CT 5.0 mSv Rx torax 2 views 0.1 mSv

Lung nodules: examples The general CAD architecture: 1.Lung segmentation: defines the area where nodules are to be detected 2.Region of Interest (ROI) hunter: identifies a list of nodule candidates 3.ROI classification (False Positive findings reduction)

RG-ACM CAD: Regiong Growing & Active Contour Model 1. Lung segmentation: Region Growing + Active Contour Model 2. Nodule candidate identification: Region growing-based iterative algorithm 3. ROI classification: Rule-based filter + Neural Network [R. Bellotti et al, Med Phys 34, (2007)] Lung segmentation Nodule candidate identification False Positive reduction Internal force F i FiFi R i =F i+1 +F i+1 +F A Adhesive force F A q=F A /F i Rule-based filter Neural network

Lung CAD FROC curves Validation dataset: 24 CT (28 nodules with diameters ≥ 5mm ) 75% 2÷6 FP/scan 0.75

Comparison with commercial systems R2 technology ImageChecker® CT Lung system (the first clinically validated CAD system for chest CT) 73% 3 FP/scan CT (140kV, 60mA, 1.25 mm slice thickness) [Roberts et al, CARS’05, pp ] ImageChecker® v1.0, 56% 3.5 FP/scan - 30 MDCT (110 kV, mA,1.25 mm slice thickness) [Brochu B, et al., J Radiol 2007;88: ] ImageChecker® (LM-1000) Sensitivity 3.2 FP/scan MDCT (120 kV, 80 mAs, 2.5 mm slice thickness) [Yuan R, et al., Am J Roentgenol 2006;186: ] ImageChecker® (LM-1000) Sensitivity 1.6 FP/scan - 70 MDCT (120 kVp, variable mAs, 2.5 mm slice thickness) [Lee IJ, et al., Korean J Radiol. 2005:89-93] Siemens Prototype of LungCAD CT 77.1% 2.7 FP/scan CT (120 kV, 90mAs, 1 mm slice thickness) [Wolf, CARS’05, pp ] ICAD Az= MDCT (120 kV, 80 mAs, 0.75 mm slice thickness) [Marten K, et al., Eur Radiol 2004;14: ] Siemens vs R2 ImageChecker® Sensitivity 6 FP/scan NEV Sensitivity 8 FP/scan 25 MDCT (120 kV, mAs, 1-2 mm slice thickness) [Das M, et al., Radiology 2006; 241: ]

Commercial and academic research systems The ANODE09 International competition for Nodule Detection in chest CT The results have been presented at SPIE Medical Imaging 2009

The early diagnosis of Alzheimer’s disease  Analysis of MR images of the brain  Evaluation of the atrophy of the hippocampus

What kind of information would be interesting for the neurophysiologist community? Early diagnosis by means of affordable and reliable tests “… a highly sensitive and specific diagnostic method for early detection of the disease is of the utmost importance for overall patient management and outcome.” years [Neurology 2002;59: ] years Overall cognitive performances for MCI subjects (arb. units) Mild Cognitive Impairment (MCI) predictors: only a fraction of the MCI population evolves in AD Evaluation of AD developers Initial conditions (all MCI) Performance decreases rapidly [34% of the initial population] Expected performance loss due to aging

Physical observables The goal is to find one (or more) observable, whose distribution:  maximizes the separation between Controls and AD population  is able to predict the evolution of a MCI patient Significant information is supposed to be encoded in the hippocampal ROI Hippocampal Boxes CSF/GM/WM Histogram equalization  Scalar product Median HB Controls Median HB AD [A. Chincarini et al, Computational Vision and Medical Image Processing, Tayolor & Francis, (2007)] Database provided by: Ospedale S. Martino, Genova the Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Analysis of MTL ROIs *

Conclusions Lung CAD  Good results  Integration of the different approaches  Database population Neuroimage analysis  Analysis of the hippocampus degree of atrophy for the early diagnosis of AD  Validation of the automated segmentation algorithm  Shape analysis  A physical observable computed on the hippocampal boxes can reliably predict the evolution of the MCI patients Contact:

Thanks for your attention! I am grateful to all the members of the MAGIC-5 Collaboration for their contribution