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Method: ProRSeg was trained using 5-fold cross-validation with 110T2-weighted MRI acquired at 5 treatment fractions from 10 different patients,taking care that same patient scans were not placed in training and testingfolds. Segmentation accuracy was measured using Dice similarity coefficient(DSC) and Hausdorff distance at 95th percentile (HD95). Registrationconsistency was measured using coefficient of variation (CV) in displacement ofOARs. Ablation tests and accuracy comparisons against multiple methods weredone. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scanswas evaluated on 80 scans using 5-fold cross-validation. Results: ProRSegprocessed 3D volumes (128 $\times$ 192 $\times$ 128) in 3 secs on a NVIDIATesla V100 GPU. It's segmentations were significantly more accurate ($p<0.001$)than compared methods, achieving a DSC of 0.94 $\pm$0.02 for liver,0.88$\pm$0.04 for large bowel, 0.78$\pm$0.03 for small bowel and 0.82$\pm$0.04for stomach-duodenum from MRI. ProRSeg achieved a DSC of 0.72$\pm$0.01 forsmall bowel and 0.76$\pm$0.03 for stomach-duodenum from CBCT. ProRSegregistrations resulted in the lowest CV in displacement (stomach-duodenum$CV_{x}$: 0.75\%, $CV_{y}$: 0.73\%, and $CV_{z}$: 0.81\%; small bowel $CV_{x}$:0.80\%, $CV_{y}$: 0.80\%, and $CV_{z}$: 0.68\%; large bowel $CV_{x}$: 0.71\%,$CV_{y}$ : 0.81\%, and $CV_{z}$: 0.75\%). ProRSeg based dose accumulationaccounting for intra-fraction (pre-treatment to post-treatment MRI scan) andinter-fraction motion showed that the organ dose constraints were violated in 4patients for stomach-duodenum and for 3 patients for small bowel. Studylimitations include lack of independent testing and ground truth phantomdatasets to measure dose accumulation accuracy.

Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT