Automatic segmentation and PI-RADS grading of prostate cancer for biparametric MRI

The annual mortality rate from prostate cancer (PCa), a common malignant neoplasm affecting middle-aged and elderly men, is on the rise. Biparametric magnetic resonance imaging (bpMRI) is indispensable to PCa imaging analysis since it can capture distinct disease-related information from two modalities that exhibit synergistic performance. The majority of state-of-the-art PCa diagnostic techniques currently available are focus on a single modality or task, neglecting the information sharing across the two modalities and task correlations inherent in multi-task learning. We provide a dual-modality image fusion and multi-task learning model that can accomplish both automatic PI-RADS grading and prostate and PCa region segmentation simultaneously. First, to extract complementary information between the prostate and PCa in bimodal images via T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) feature extraction, a shared block fusion module and an independent encoder block were developed; Subsequently, in the encoder stage, the dual visual attention module was designed to extract features from multiple receptive field and deliver more accurate contextual information, and a novel decoder was designed to effectively integrate encoder features, yielding more refined global and local detail information; Next, to capture more precise detail information during the classification task stage, a high-level feature fusion technique was developed; To address class imbalance, a multitask mixed loss function is finally suggested. The segmentation results of prostate and PCa on multiple diverse male pelvic MRI datasets demonstrate the superior performance of our proposed method. Both the basic performance evaluation and comparative model evaluation of the proposed model have validated its effectiveness in prostate and PCa segmentation as well as PI-RADS automatic grading. External validation on the independent PROMISE12 dataset further confirms the strong generalizability of our model across different institutions, scanning devices and patient cohorts.