Micro-ultrasound (microUS) is a non-inferior alternative to multi-parametric MRI for clinically significant prostate cancer (csPCa) diagnosis, yet cancer’s subtle features result in missed cancers and large inter-reader variability. We seek to automate the detection of csPCa on microUS by developing the microUS Prostate Cancer Detection Network (MUSegNet). Methods Implemented using the well-established segmentation framework, nnUnet, MUSegNet automatically outlines the prostate, transitional zone, and csPCa. Our study explored strategies to train such segmentation models given reduced data, which is common with new imaging modalities such as microUS and/or when labeling data is tedious. MUSegNet was trained using three datasets: (1) 53 microUS exams manually labeled using MRI and pathology references (53 patients), (2) 565 unlabeled microUS exams (106 patients), and (3) 2749 conventional b-mode ultrasound exams (2281 patients). We evaluated MUSegNet in 73 independent patients from two institutions, against manual labels and in direct comparison with six expert readers. Results MUSegNet was significantly better at detecting csPCa than the baseline models trained using manual labels (area under the receiver operating characteristic curve – AUROC; 0.85 vs. 0.73, Delong p-value < 0.01, sensitivity: 0.74 vs. 0.27, dice similarity coefficient: 0.29 vs. 0.09, Wilcoxon p-value < 0.001). MUSegNet was better at detecting posterior...