A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered. One pipeline uses random forest on individual sequences while the other uses an image filter described to produce probability map-like images. These were compared to a previously published CAD approach based on support vector machine (SVM) evaluated on the same data. The random forest, features, sampling strategy, and instance-level weighting improve prostate cancer detection performance [area under the curve (AUC) 0.93] in comparison to SVM (AUC 0.86) on the same test data. Using a simple image filtering technique as a first-stage detector to highlight likely regions of prostate cancer helps with learning stability over using a learning-based approach owing to visibility and ambiguity of annotations in each sequence.
Prostate cancer (PCa) is the second most common cause of cancer related deaths in men. Multiparametric MRI (mpMRI) is the most accurate imaging method for PCa detection; however, it requires the expertise of experienced radiologists leading to inconsistency across readers of varying experience. To increase inter-reader agreement and sensitivity, we developed a computer-aided detection (CAD) system that can automatically detect lesions on mpMRI that readers can use as a reference. We investigated a convolutional neural network based deep-learing (DCNN) architecture to find an improved solution for PCa detection on mpMRI. We adopted a network architecture from a state-of-the-art edge detector that takes an image as an input and produces an image probability map. Two-fold cross validation along with a receiver operating characteristic (ROC) analysis and free-response ROC (FROC) were used to determine our deep-learning based prostate-CAD’s (CADDL) performance. The efficacy was compared to an existing prostate CAD system that is based on hand-crafted features, which was evaluated on the same test-set. CADDL had an 86% detection rate at 20% false-positive rate while the top-down learning CAD had 80% detection rate at the same false-positive rate, which translated to 94% and 85% detection rate at 10 false-positives per patient on the FROC. A CNN based CAD is able to detect cancerous lesions on mpMRI of the prostate with results comparable to an existing prostate-CAD showing potential for further development.