Medical College of Wisconsin
CTSICores SearchResearch InformaticsREDCap

A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging-Guided Online Adaptive Radiation Therapy: A Feasibility Study. Adv Radiat Oncol 2020;5(6):1350-1358

Date

12/12/2020

Pubmed ID

33305098

Pubmed Central ID

PMC7718500

DOI

10.1016/j.adro.2020.04.027

Scopus ID

2-s2.0-85097125143   14 Citations

Abstract

PURPOSE: Magnetic resonance-guided online adaptive radiation therapy (MRgOART) requires accurate and efficient segmentation. However, the performance of current autosegmentation tools is generally poor for magnetic resonance imaging (MRI) owing to day-to-day variations in image intensity and patient anatomy. In this study, we propose a patient-specific autosegmentation strategy using multiple-input deformable image registration (DIR; PASSMID) to improve segmentation accuracy and efficiency for MRgOART.

METHODS AND MATERIALS: Longitudinal MRI scans acquired on a 1.5T MRI-Linac for 10 patients with abdominal cancer were used. The proposed PASSMID includes 2 steps: applying a patient-specific image processing pipeline to longitudinal MRI scans, and populating all contours from previous sessions/fractions to a new fractional MRI using multiple DIRs and combining the resulted contours using simultaneous truth and performance level estimation (STAPLE) to obtain the final consensus segmentation. Five contour propagation strategies were compared: planning computed tomography to fractional MRI scans through rigid body registration (RDR), pretreatment MRI to fractional MRI scans through RDR and DIR, and the proposed multi-input DIR/STAPLE without preprocessing, and the PASSMID. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) with ground truth contours were calculated slice by slice to quantify the contour accuracy. A quantitative index, defined as the ratio of acceptable slices, was introduced using a criterion of DSC > 0.8 and MDA < 2 mm.

RESULTS: The proposed PASSMID performed well with an average 2-dimensional DSC/MDA of 0.94/1.78 mm, 0.93/1.04 mm, 0.93/1.06 mm, 0.93/1.14 mm, 0.92/0.83 mm, 0.84/1.53 mm, 0.86/2.39 mm, 0.81/2.49 mm, 0.72/5.48 mm, and 0.70/5.03 mm for the liver, left kidney, right kidney, spleen, aorta, pancreas, stomach, duodenum, small bowel, and colon, respectively. Starting from the third fractions, the contour accuracy was significantly improved with PASSMID compared with the single-DIR strategy (P < .05). The mean ratio of acceptable slices were 13.9%, 17.5%, 60.8%, 70.6%, and 71.8% for the 5 strategies, respectively.

CONCLUSIONS: The proposed PASSMID solution, by combining image processing, multi-input DIRs, and STAPLE, can significantly improve the accuracy of autosegmentation for intrapatient MRI scans, reducing the time required for further contour editing, thereby facilitating the routine practice of MRgOART.

Author List

Zhang Y, Paulson E, Lim S, Hall WA, Ahunbay E, Mickevicius NJ, Straza MW, Erickson B, Li XA

Authors

Ergun Ahunbay PhD Professor in the Radiation Oncology department at Medical College of Wisconsin
Beth A. Erickson MD Professor in the Radiation Oncology department at Medical College of Wisconsin
William Adrian Hall MD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin
Xiaochuan Allen Li PhD Professor in the Radiation Oncology department at Medical College of Wisconsin
Nikolai J. Mickevicius PhD Assistant Professor in the Radiology department at Medical College of Wisconsin
Michael W. Straza MD, PhD Assistant Professor in the Radiation Oncology department at Medical College of Wisconsin