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

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 <.01). Leveraging fractionated data for augmentation improved over a single MR simulation time point (0.579 ± 0.29; P <.01). Predicted contours yielded dose-volume histograms that closely matched those of the clinical treatment plans where mean and maximum (ie, dose to 0.03 cc) doses deviated by 0.32 ± 0.5 Gy and 1.42 ± 2.6 Gy, respectively. There were no statistically significant differences between institute A and B volumes (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.

MAGNET: A MODALITY-AGNOSTIC NETWORK FOR 3D MEDICAL IMAGE SEGMENTATION.

In this paper, we proposed MAGNET, a novel modality-agnostic network for 3D medical image segmentation. Different from existing learning methods, MAGNET is specifically designed to handle real medical situations where multiple modalities/sequences are available during model training, but fewer ones are available or used at time of clinical practice. Our results on multiple datasets show that MAGNET trained on multi-modality data has the unique ability to perform predictions using any subset of training imaging modalities. It outperforms individually trained uni-modality models while can further boost performance when more modalities are available at testing.