International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

This review describes fundamentals of multiparametric MR imaging for prostate cancer and provides an overview of separate MR imaging techniques with their combined merits and limitations for clinical challenges such as detection, localization, local staging, and active surveillance. This review presents the current state of the art regarding multiparametric magnetic resonance (MR) imaging of prostate cancer. Technical requirements and clinical indications for the use of multiparametric MR imaging in detection, localization, characterization, staging, biopsy guidance, and active surveillance of prostate cancer are discussed. Although reported accuracies of the separate and combined multiparametric MR imaging techniques vary for diverse clinical prostate cancer indications, multiparametric MR imaging of the prostate has shown promising results and may be of additional value in prostate cancer localization and local staging. Consensus on which technical approaches (field strengths, sequences, use of an endorectal coil) and combination of multiparametric MR imaging techniques should be used for specific clinical indications remains a challenge. Because guidelines are currently lacking, suggestions for a general minimal protocol for multiparametric MR imaging of the prostate based on the ...

Prostate cancer is one of the major causes of cancer death for men in the western world. Magnetic resonance imaging (MRI) is being increasingly used as a modality to detect prostate cancer. Therefore, computer-aided detection of prostate cancer in MRI images has become an active area of research. In this paper we investigate a fully automated computer-aided detection system which consists of two stages. In the first stage, we detect initial candidates using multi-atlas-based prostate segmentation, voxel feature extraction, classification and local maxima detection. The second stage segments the candidate regions and using classification we obtain cancer likelihoods for each candidate. Features represent pharmacokinetic behavior, symmetry and appearance, among others. The system is evaluated on a large consecutive cohort of 347 patients with MR-guided biopsy as the reference standard. This set contained 165 patients with cancer and 182 patients without prostate cancer. Performance evaluation is based on lesion-based free-response receiver operating characteristic curve and patient-based receiver operating characteristic analysis. The system is also compared to the prospective clinical performance of radiologists. Results show a sensitivity of 0.42, 0.75, and 0.89 at 0.1, 1, and 10 false positives per normal case. In clinical workflow the system could potentially be used to improve the sensitivity of the radiologist. At the high specificity reading setting, which is typical in screening situations, the system does not perform significantly different from the radiologist and could be used as an independent second reader instead of a second radiologist. Furthermore, the system has potential in a first-reader setting.

Purpose: To prospectively determine the accuracies of T2-weighted magnetic resonance (MR) imaging, dynamic contrast material–enhanced MR imaging, and quantitative three-dimensional (3D) proton MR spectroscopic imaging of the entire prostate for prostate cancer localization, with whole-mount histopathologic section findings as the reference standard. Materials and Methods: This study was approved by the institutional review board, and informed consent was obtained from all patients. Thirty-four consecutive men with a mean age of 60 years and a mean prostate-specific antigen level of 8 ng/mL were examined. The median biopsy Gleason score was 6. T2-weighted MR imaging, dynamic contrast-enhanced MR imaging, and 3D MR spectroscopic imaging were performed, and on the basis of the image data, two readers with different levels of experience recorded the location of the suspicious peripheral zone and central gland tumor nodules on each of 14 standardized regions of interest (ROIs) in the prostate. The degree of di...

To evaluate which parameters of dynamic magnetic resonance (MR) imaging and T2 relaxation rate would result in optimal discrimination of prostatic carcinoma from normal peripheral zone (PZ) and central gland (CG) tissues and to correlate these parameters with tumor stage, Gleason score, patient age, and tumor markers. Of 58 patients with prostatic carcinoma, 36 were included for analysis. Patients underwent MR imaging at 1.5 T with an endorectal-pelvic phased-array coil and subsequently underwent prostatectomy. A T2-weighted turbo spin-echo sequence, an intermediate-weighted sequence, and a fast T1-weighted gradient-echo sequence (seven sections in 2.03 seconds) during bolus injection of 0.1 mmol gadopentetate dimeglumine per kilogram of body weight were performed. Contrast agent concentration-time curves were obtained for prostatic carcinoma and normal PZ and CG tissue by using whole-mount sections to guide placement of regions of interest. Onset time, time to peak, peak enhancement, relative peak enhancement, washout, and T2 relaxation rates were calculated. Multivariate receiver operating characteristic analysis was performed with and without relative peak enhancement. Results of multivariate receiver operating characteristic analysis showed that relative peak enhancement demonstrated the highest area under the receiver operating characteristic curve (AUC) in the PZ and the CG (AUC = 0.93, 0.82). Results of multivariate analysis without relative peak enhancement showed that relative peak enhancement in the PZ and washout in the CG demonstrated the highest AUC (AUC = 0.9, 0.81). Pearson correlation coefficients between the dynamic parameters or T2 relaxation rates in carcinoma and the tumor stage, Gleason score, patient age, and tumor markers ranged between 0.02 and 0.44. The optimal parameter for discrimination of prostatic carcinoma in the PZ and CG was relative peak enhancement. If relative peak enhancement was not used, then peak enhancement was optimal in the PZ, and washout was optimal in the CG. Poor-to-moderate correlation was present between the dynamic parameters or T2 relaxation rate in carcinoma and the tumor stage, Gleason score, patient age, tumor volume, and prostate-specific antigen.