GPCALMA: implementation in Italian hospitals of a computer aided detection system for breast lesions by mammography examination.

We describe the implementation in several Italian hospitals of a computer aided detection (CAD) system, named GPCALMA (grid platform for a computer aided library in mammography), for the automatic search of lesions in X-ray mammographies. GPCALMA has been under development since 1999 by a community of physicists of the Italian National Institute for Nuclear Physics (INFN) in collaboration with radiologists. This CAD system was tested as a support to radiologists in reading mammographies. The main system components are: (i) the algorithms implemented for the analysis of digitized mammograms to recognize suspicious lesions, (ii) the database of digitized mammographic images, and (iii) the PC-based digitization and analysis workstation and its user interface. The distributed nature of data and resources and the prevalence of geographically remote users suggested the development of the system as a grid application: the design of this networked version is also reported. The paper describes the system architecture, the database of digitized mammographies, the clinical workstation and the medical applications carried out to characterize the system. A commercial CAD was evaluated in a comparison with GPCALMA by analysing the medical reports obtained with and without the two different CADs on the same dataset of images: with both CAD a statistically significant increase in sensitivity was obtained. The sensitivity in the detection of lesions obtained for microcalcification and masses was 96% and 80%, respectively. An analysis in terms of receiver operating characteristic (ROC) curve was performed for massive lesion searches, achieving an area under the ROC curve of A(z)=0.783+/-0.008. Results show that the GPCALMA CAD is ready to be used in the radiological practice, both for screening mammography and clinical studies. GPCALMA is a starting point for the development of other medical imaging applications such as the CAD for the search of pulmonary nodules, currently under development in the framework of an INFN-funded project.

The Continuous Motion Technique for a New Generation of Scanning Systems.

In the present paper we report the development of the Continuous Motion scanning technique and its implementation for a new generation of scanning systems. The same hardware setup has demonstrated a significant boost in the scanning speed, reaching 190 cm2/h. The implementation of the Continuous Motion technique in the LASSO framework, as well as a number of new corrections introduced are described in details. The performance of the system, the results of an efficiency measurement and potential applications of the technique are discussed.

A massive lesion detection algorithm in mammography.

A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps: 1) reduction of the dimension of the image to be processed through the identification of regions of interest (roi) as candidates for massive lesions; 2) characterization of the RoI by means of suitable feature extraction; 3) pattern classification through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defined fraction of the maximum. The ROIS thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at different fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the INFN (Istituto Nazionale Fisica Nucleare) research project GPCALMA (Grid Platform for Calma) which recruits physicists and radiologists from different Italian Research Institutions and hospitals to develop software for breast cancer detection.

Comparison of standard reading and computer aided detection (CAD) on a national proficiency test of screening mammography.

OBJECTIVE: To evaluate the role of computer aided detection (CAD) in improving the interpretation of screening mammograms MATERIAL AND METHODS: Ten radiologists underwent a proficiency test of screening mammography first by conventional reading and then with the help of CAD. Radiologists were blinded to test results for the whole study duration. Results of conventional and CAD reading were compared in terms of sensitivity and recall rate. Double reading was simulated combining conventional readings of four expert radiologists and compared with CAD reading. RESULTS: Considering all ten readings, cancer was identified in 146 or 153 of 170 cases (85.8 vs. 90.0%; chi(2)=0.99, df=1, P=0.31) and recalls were 106 or 152 of 1330 cases (7.9 vs. 11.4%; chi(2)=8.69, df=1, P=0.003) at conventional or CAD reading, respectively. CAD reading was essentially the same (sensitivity 97.0 vs. 96.0%; chi(2)=7.1, df=1, P=0.93; recall rate 10.7 vs. 10.6%; chi(2)=1.5, df=1, P=0.96) as compared with simulated conventional double reading. CONCLUSION: CAD reading seems to improve the sensitivity of conventional reading while reducing specificity, both effects being of limited size. CAD reading had almost the same performance of simulated conventional double reading, suggesting a possible use of CAD which needs to be confirmed by further studies inclusive of cost-effective analysis.