Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning

Document Type

Article

Publication Date

11-1-2024

Publication Title

International journal of radiation oncology, biology, physics

Abstract

PURPOSE: Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than to whole-heart metrics. Magnetic resonance (MR)-guided radiation therapy (MRgRT) enables substructure visualization during daily localization, allowing potential for enhanced cardiac sparing. We extend a publicly available state-of-the-art deep learning framework, "No New" U-Net, to incorporate self-distillation (nnU-Net.wSD) for substructure segmentation for MRgRT.

METHODS AND MATERIALS: Eighteen (institute A) patients who underwent thoracic or abdominal radiation therapy on a 0.35 T MR-guided linear accelerator were retrospectively evaluated. On each image, 1 of 2 radiation oncologists delineated reference contours of 12 cardiac substructures (chambers, great vessels, and coronary arteries) used to train (n = 10), validate (n = 3), and test (n = 5) nnU-Net.wSD by leveraging a teacher-student network and comparing it to standard 3-dimensional U-Net. The impact of using simulation data or including 3 to 4 daily images for augmentation during training was evaluated for nnU-Net.wSD. Geometric metrics (Dice similarity coefficient, mean distance to agreement, and 95% Hausdorff distance), visual inspection, and clinical dose-volume histograms were evaluated. To determine generalizability, institute A's model was tested on an unlabeled data set from institute B (n = 22) and evaluated via consensus scoring and volume comparisons.

RESULTS: nnU-Net.wSD yielded a Dice similarity coefficient (reported mean ± SD) of 0.65 ± 0.25 across the 12 substructures (chambers, 0.85 ± 0.05; great vessels, 0.67 ± 0.19; and coronary arteries, 0.33 ± 0.16; mean distance to agreement, <3 >mm; mean 95% Hausdorff distance, <9 >mm) while outperforming the 3-dimensional U-Net (0.583 ± 0.28; P.05) for 11 of 12 substructures, with larger volumes requiring minor changes and coronary arteries exhibiting more variability.

CONCLUSIONS: This work is a critical step toward rapid and reliable cardiac substructure segmentation to improve cardiac sparing in low-field MRgRT.

Medical Subject Headings

Humans; Deep Learning; Radiotherapy, Image-Guided; Heart; Retrospective Studies; Magnetic Resonance Imaging; Organs at Risk; Abdominal Neoplasms; Male; Radiotherapy Planning, Computer-Assisted; Radiotherapy Dosage; Thoracic Neoplasms

PubMed ID

38797498

ePublication

ePub ahead of print

Volume

120

Issue

3

First Page

904

Last Page

914

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