Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT Journal Article
| Authors: | Jiang, J.; Hong, J.; Tringale, K.; Reyngold, M.; Crane, C.; Tyagi, N.; Veeraraghavan, H. |
| Article Title: | Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT |
| Abstract: | Background: Adaptive radiation treatment (ART) for locally advanced pancreatic cancer (LAPC) requires consistently accurate segmentation of the extremely mobile gastrointestinal (GI) organs at risk (OAR) including the stomach, duodenum, large and small bowel. Also, due to lack of sufficiently accurate and fast deformable image registration (DIR), accumulated dose to the GI OARs is currently only approximated, further limiting the ability to more precisely adapt treatments. Purpose: Develop a 3-D Progressively refined joint Registration-Segmentation (ProRSeg) deep network to deformably align and segment treatment fraction magnetic resonance images (MRI)s, then evaluate segmentation accuracy, registration consistency, and feasibility for OAR dose accumulation. Method: ProRSeg was trained using five-fold cross-validation with 110 T2-weighted MRI acquired at five 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. Statistical comparison to other deep learning and iterative registration methods were done using the Kruskal-Wallis test, followed by pair-wise comparisons with Bonferroni correction applied for multiple testing. Ablation tests and accuracy comparisons against multiple methods were done. Finally, applicability of ProRSeg to segment cone-beam CT (CBCT) scans was evaluated on a publicly available dataset of 80 scans using five-fold cross-validation. Results: ProRSeg processed 3D volumes (128 × 192 × 128) in 3 s on a NVIDIA Tesla V100 GPU. It's segmentations were significantly more accurate ((Figure presented.)) than compared methods, achieving a DSC of 0.94 ±0.02 for liver, 0.88±0.04 for large bowel, 0.78±0.03 for small bowel and 0.82±0.04 for stomach-duodenum from MRI. ProRSeg achieved a DSC of 0.72±0.01 for small bowel and 0.76±0.03 for stomach-duodenum from public CBCT dataset. ProRSeg registrations resulted in the lowest CV in displacement (stomach-duodenum (Figure presented.) : 0.75%, (Figure presented.) : 0.73%, and (Figure presented.) : 0.81%; small bowel (Figure presented.) : 0.80%, (Figure presented.) : 0.80%, and (Figure presented.) : 0.68%; large bowel (Figure presented.) : 0.71%, (Figure presented.) : 0.81%, and (Figure presented.) : 0.75%). ProRSeg based dose accumulation accounting for intra-fraction (pre-treatment to post-treatment MRI scan) and inter-fraction motion showed that the organ dose constraints were violated in four patients for stomach-duodenum and for three patients for small bowel. Study limitations include lack of independent testing and ground truth phantom datasets to measure dose accumulation accuracy. Conclusions: ProRSeg produced more accurate and consistent GI OARs segmentation and DIR of MRI and CBCTs compared to multiple methods. Preliminary results indicates feasibility for OAR dose accumulation using ProRSeg. © 2023 American Association of Physicists in Medicine. |
| Keywords: | magnetic resonance imaging; radiotherapy; registration; computerized tomography; magnetic resonance; segmentation; mri; image segmentation; cbct; organs at risks; gastrointestinal; cone-beam ct; small bowel; iterative methods; deep learning; dose accumulation; gi organs; recurrent deep networks; gastrointestinal organ; magnetic resonance image; recurrent deep network |
| Journal Title: | Medical Physics |
| Volume: | 50 |
| Issue: | 8 |
| ISSN: | 0094-2405 |
| Publisher: | American Association of Physicists in Medicine |
| Date Published: | 2023-08-01 |
| Start Page: | 4758 |
| End Page: | 4774 |
| Language: | English |
| DOI: | 10.1002/mp.16527 |
| PUBMED: | 37265185 |
| PROVIDER: | scopus |
| PMCID: | PMC11009869 |
| DOI/URL: | |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PubMed and PDF -- MSK corresponding author is Harini Veeraraghavan -- Source: Scopus |
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