PARALLEL ROUTE COHERENT MIXUP DEEP LEARNING FOR SINGLE SOURCE TO MULTI-DOMAIN GENERALIZED PROSTATE CANCER SEGMENTATION FOR MR GUIDED ADAPTIVE RADIOTHERAPY.

Magnetic resonance imaging (MRI) guided adaptive radiotherapy for prostate cancer (PCa) can allow to adapt treatment every day to the geometry of tumor and organs to deliver precise treatments while sparing organs of radiation. However, manual delineation of tumors and organs limits the adoption of this technology. Deep learning (DL) methods, though promising for in-distribution (ID) data that resembles training, deteriorate in performance when applied to out-of-distribution (OOD) datasets. Hence, we developed a novel parallel and coherent feature mixup (PaRC-mix) training approach for single source to multi-domain generalization. PaRC-mix creates feature augmentations at multiple network layers through matching linear combination of features extracted across training batches. PaRC-mix training was implemented in UNet++ to segment PCa from apparent diffusion coefficient images and compared to other mixup approaches. Models were trained on 2,029 samples from 3.0T GE MRI and tested on 557 PCa samples from 3 datasets used for diagnosis, treatment planning, and during radiation scans acquired using 3T Siemens, 3T Philips, and 1.5T Elekta Unity MR-Linac scanners from public and institutional datasets. PaRC-mix training led to significantly more accurate tumor detection (p < 0.001) and segmentation (p < 0.001) compared to other methods.