Introduction: Multiparametric magnetic resonance imaging (mpMRI) is an evolving tool in bladder cancer (BC) staging, specifically for detection of muscle invasion. In this regard, deep learning (DL) models potentially could improve the accuracy of automatic multi-region bladder cancer mpMRI segmentation. Our previous work demonstrated the feasibility of fully automated high accuracy multiregional MRI segmentation of BC on T2-weighted (T2-WI) images. This project evaluates the performance of DL models on mpMRI. Methods: After IRB approval, 33 patients with bladder masses were prospectively evaluated with a 3 Tesla pelvic mpMRI . mpMRI consisted of high resolution T2-WI, diffusion weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted dynamic contrast enhanced (DCE) sequences. For each dataset, tumor, inner wall and outer wall of bladder were manually delineated by two experts (KG, DG) using ITK-Snap software. Four different models (Unet, MAnet, PSPNet, Unet++) with three different learning objectives (i.e., cross-entropy (CE), dice loss or a compounded version of both) were evaluated utilizing multi-modal data incorporating (T2-W, ADC, DCE). Similarity between segmentations was evaluated with Dice similarity coefficient (DSC) and Hausdorff distance (HD). The models were trained with 27 patients and tested on the remaining 6 patients. Results: The model UNet++ trained with the compounded loss provided the best results consistently in terms of DSC and HD. PSPNet obtained much smaller HD distances on the tumor on DWI. The results represented a strong agreement with reference contours. Overall, the models achieved better segmentation of the regions on DWI/ADC and T1-W images than on the T2-WI images (Table 1). Conclusions: Our preliminary results demonstrate DL networks can provide automatic muti-region segmentation of BC on mpMRI images. Unet++ provided the best performance of the four DL models in segmenting tumor mass, and bladder walls. Future work can develop more accurate algorithms improving detection of BC lesions based on mpMRI sequences. SOURCE OF Funding: University of Florida UFII SEED Funds
