Proc. SPIE. 8670, Medical Imaging 2013: Computer-Aided Diagnosis
KEYWORDS: Signal to noise ratio, Principal component analysis, Computer aided diagnosis and therapy, Cancer, Magnetic resonance imaging, Image segmentation, Image registration, Prostate, Prostate cancer, CAD systems
Prostate specific antigen (PSA)-based screening reduces the rate of death from prostate cancer (PCa) by 31%, but this
benefit is associated with a high risk of overdiagnosis and overtreatment. As prostate transrectal ultrasound-guided
biopsy, the standard procedure for prostate histological sampling, has a sensitivity of 77% with a considerable false-negative rate, more accurate methods need to be found to detect or rule out significant disease. Prostate magnetic
resonance imaging has the potential to improve the specificity of PSA-based screening scenarios as a non-invasive
detection tool, in particular exploiting the combination of anatomical and functional information in a multiparametric
framework. The purpose of this study was to describe a computer aided diagnosis (CAD) method that automatically
produces a malignancy likelihood map by combining information from dynamic contrast enhanced MR images and
diffusion weighted images. The CAD system consists of multiple sequential stages, from a preliminary registration of images of different sequences, in order to correct for susceptibility deformation and/or movement artifacts, to a Bayesian classifier, which fused all the extracted features into a probability map. The promising results (AUROC=0.87) should be validated on a larger dataset, but they suggest that the discrimination on a voxel basis between benign and malignant tissues is feasible with good performances. This method can be of benefit to improve the diagnostic accuracy of the radiologist, reduce reader variability and speed up the reading time, automatically highlighting probably cancer suspicious regions.
Computer-aided diagnosis (CAD) systems using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI)
data may be developed to help localize prostate cancer and guide biopsy, avoiding random sampling of the whole gland.
The purpose of this study is to present a DCE-MRI CAD system, which calculates the likelihood of malignancy in a
given area of the prostate by combining model-based and model-free parameters. The dataset includes 10 patients with
prostate cancer, with a total of 13 foci of adenocarcinoma. The post-processing is based on the following steps: testing of
registration quality, noise filtering, and extracting the proposed features needed to the CAD. Parameters with the best
performance in discriminating between normal and cancer regions are selected by computing the area under the ROC
curve, and by evaluating the correlation between pairs of features. A 6-dimensional parameters vector is generated for
each pixel and fed into a Bayesian classifier, in which the output is the probability of malignancy. The classification
performance is estimated using the leave-one-out method. The resulting area under the ROC curve is 0.899
(95%CI:0.893-0.905); sensitivity and specificity are 82.4% and 82.1% respectively at the best cut-off point (0.352).
Preliminary results show that the system is accurate in detecting areas of the gland that are involved by tumor. Further
studies will be necessary to confirm these promising preliminary results.
Proc. SPIE. 7260, Medical Imaging 2009: Computer-Aided Diagnosis
KEYWORDS: Breast, Computer aided diagnosis and therapy, Magnetic resonance imaging, Image segmentation, Image processing, Image restoration, Image registration, Medical imaging, Mammography, Current controlled current source
Dynamic Contrast Enhanced MRI (DCE-MRI) has today a well-established role, complementary to routine imaging techniques for breast cancer diagnosis such as mammography. Despite its undoubted clinical advantages, DCE-MRI data analysis is time-consuming and Computer Aided Diagnosis (CAD) systems are required to help radiologists. Segmentation is one of the key step of every CAD image processing pipeline, but most techniques available require human interaction.
We here present the preliminary results of a fully automatic lesion detection method, capable of dealing with fat suppression image acquisition sequences, which represents a challenge for image processing algorithms due to the low SNR. The method is based on four fundamental steps: registration to correct for motion artifacts; anatomical segmentation to discard anatomical structures located outside clinically interesting lesions; lesion detection to select enhanced areas and false positive reduction based on morphological and kinetic criteria. The testing set was composed by 13 cases and included 27 lesions (10 benign and 17 malignant) of diameter > 5 mm. The system achieves a per-lesion sensitivity of 93%, while yielding an acceptable number of false positives (26 on average). The results of our segmentation algorithm were verified by visual inspection, and qualitative comparison with a manual segmentation yielded encouraging results.