An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence.

A substantial percentage of prostate cancer cases are overdiagnosed and overtreated due to the challenge in deter- mining aggressiveness. Multi-parametric MR is a powerful imaging technique to capture distinct characteristics of prostate lesions that are informative for aggressiveness assessment. However, manual interpretation requires a high level of expertise, is time-consuming, and significant inter-observer variation exists for radiologists. We propose a completely automated approach to assessing pixel-level aggressiveness of prostate cancer in multi-parametric MRI. Our model efficiently combines traditional computer vision and deep learning algorithms, to remove reliance on manual features, prostate segmentation, and prior lesion detection and identified optimal combinations of MR pulse sequences for assessment. Using ADC and DWI, our proposed model achieves ROC-AUC of 0.86 and ROC-AUC of 0.88 for the diagnosis of aggressive and non-aggressive prostate lesions, respectively. In performing pixel-level clas- sification, our model's classifications are easily interpretable and allow clinicians to infer localized analyses of the lesion.