Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network

Abstract

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

“…Moreover, CNNs were used with patch-based ensemble learning [33] or dense prediction schemes [34]. In addition, end-to-end deep neural networks achieved outstanding results in automated PCa detection in multi-parametric MRI [35,36].…”

Prostate Cancer Inference via Weakly-Supervised Learning using a Large Collection of Negative MRI

“…We also evaluated the lesion localization performance using the free-response receiver operating characteristic (FROC) analysis [6,15]. The PCa localization points were determined by the local maximums of the suspiciousness map [15]. A localization point was considered as a true positive if it is within 5mm of a ground truth lesion ROI, or it is otherwise a false negative [9].…”

Semi-supervised mp-MRI data synthesis with StitchLayer and auxiliary distance maximization

“…This study was approved by our local institutional review board. The mp-MRI data used in the study were collected from two datasets: 1) A locally collected dataset named TJPCa Dataset [7], [8], [6] includes data conforming to the following five criteria: 1) the data for PCa assessment were acquired between June 2014 and December 2015; 2) all data included either pathologically-proven PCa or benign prostatic hyperplasia (BPH) by a 12-core systematic TRUS guided plus targeted prostate biopsy which were performed within six weeks after the MRI examination; 3) the data were from the patients who did not receive focal therapy, hormones, or radiation prior the MRI scan; 4) the data include both ADC and T2w images; and 5) the imaging data do not include severe artifacts that made the examination nondiagnostic. [30], [4], [31], includes data of 70 MRItargeted biopsy-proven CS PCa and 134 nonCS PCa patients.…”