Computer-Aided Detection of Clinically Significant Prostate Cancer using Bi-Parametric Magnetic Resonance Imaging

Prostate cancer (PCa) is the most common cancer among men in nearly two-thirds of countries worldwide (118 out of 195). In 2022, PCa accounted for around 1.5 million new cases and 397,000 deaths globally, making it the second most frequent cancer and the fifth leading cause of cancer-related deaths among men. A major challenge in diagnosing PCa is distinguishing benign tumors that remain non-progressive from clinically significant PCa (csPCa), which has the potential to rapidly develop into metastasis and result in death. Recently, magnetic resonance imaging (MRI) has shown high accuracy in PCa detection and characterization. However, due to the strong resemblance between csPCa and numerous nonmalignant conditions, manually characterizing of focal prostate lesions in MRI sequences is time-consuming and demands a high level of expertise. Besides, the subjective criteria used for grading could result in low inter-reader agreement. These issues raise the need of developing computer-aided diagnosis (CAD) systems to support radiologists in the automatic detection of PCa on MRI. With the increasing of large available datasets, convolutional neural networks (CNNs) have become extensively applied in PCa detection, while they are limited in learning longrange relationships due to the inherent locality of convolutional kernels. Transformer, which is notable for its ability of global context modeling, has recently shown promising improvements over CNNs in medical image processing field, but it is rarely explored for PCa detection. Furthermore, the standard self-attention mechanism used to build the Transformer is memory and computationally inefficient, which hurts its performance and limits its application in actual clinical practice. In this study, we proposed a hybrid segmentation network that effectively combines CNN and Transformer as the core component of our CAD system for PCa detection. We first introduce a memory and computationally efficient self-attention module designed to facilitate reasoning on high-resolution features, thereby improving the efficiency of learning global information while effectively capturing the details of features. Then, the encoder part consisting of three independent residualblock based branches is used to extract modality-specific features from T2W, ADC, and DWI sequences and fuse them at multi-scale levels. The following decoder uses a top-down path to progressively restore the spatial details of low-resolution feature maps and integrate multi-level features from the encoder into the highest-resolution feature map. Upon the topmost feature from the decoder, an efficient self-attention based Transformer branch is utilized to learn dense global context information. Finally, we integrate the 2D and 3D versions of our proposed network to learn both inter-slice