Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT
Jue Jiang, Jun Hong, Kathryn Tringale, Marsha Reyngold, Christopher, Crane, Neelam Tyagi, Harini Veeraraghavan

TL;DR
ProRSeg is a deep learning method that accurately segments and registers gastrointestinal organs in MRI and CBCT scans, enabling precise dose accumulation analysis during radiotherapy.
Contribution
This study introduces ProRSeg, a novel progressive refinement deep joint registration and segmentation framework for gastrointestinal organs in MRI and CBCT, with improved accuracy and speed.
Findings
ProRSeg achieved high segmentation accuracy with DSC up to 0.94 for liver.
ProRSeg demonstrated significantly better performance than existing methods.
ProRSeg enabled effective dose accumulation accounting for organ motion.
Abstract
Method: ProRSeg was trained using 5-fold cross-validation with 110 T2-weighted MRI acquired at 5 treatment fractions from 10 different patients, taking care that same patient scans were not placed in training and testing folds. Segmentation accuracy was measured using Dice similarity coefficient (DSC) and Hausdorff distance at 95th percentile (HD95). Registration consistency was measured using coefficient of variation (CV) in displacement of OARs. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans was evaluated on 80 scans using 5-fold cross-validation. Results: ProRSeg processed 3D volumes (128 192 128) in 3 secs on a NVIDIA Tesla V100 GPU. It's segmentations were significantly more accurate () than compared methods, achieving a DSC of 0.94 0.02 for liver,…
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Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · Medical Imaging Techniques and Applications · Advanced MRI Techniques and Applications
