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Updated: May 5, 2026
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of
Reza Kalantar1,2, Manasi Ingle1,2, Romelie Rieu1,2
1The Institute of Cancer Research, London, United Kingdom.
Purpose:
The integration of magnetic resonance imaging into radiation therapy (RT) treatment necessitates automated segmentation algorithms for fast and accurate adaptive interventions, particularly in magnetic resonance imaging-integrated linear accelerator (MR-linac or MRL) treatment systems. However, the scarcity of data hampers the training of these models. This study aimed to address this shortcoming by developing a synthetic MRL-assisted deep learning framework to establish a robust baseline for organ at risk segmentation on MRL images and enable domain adaptation for automatic delineations during adaptive RT treatments.
Methods And Materials:
We used a retrospective data set, comprising 158 patients diagnosed with various gynecologic cancers who underwent computed tomography scanning for RT planning and 25 patients with T2-weighted MRL scans for model fine-tuning, adaptation, and evaluation. A patch-based cycle-consistent generative adversarial network was developed to synthesize MRL images from computed tomography data. Subsequently, a domain-adaptive segmentation network was trained to segment the 6 organs at risk on acquired MRL images. In addition, we employed per-fraction adaptation to enhance anatomical conformity guided by prior treatment fractions of individual patients. A quantitative evaluation and blinded human reader assessment were conducted to establish contour acceptance rates.
Results:
The synthetic MRL-assisted model improved organ at risk segmentation accuracy on MRL images, with fraction-adapted contours displaying high anatomical fidelity. Two radiation oncologists reported contour acceptance rates of 100% and 98% for treatment planning after adaptation.
Conclusions:
This novel framework holds promise to bridge the semantic gap between computed tomography and magnetic resonance imaging databases, potentially facilitating adaptive RT treatments and reducing treatment times as well as clinician burden. The utility of this framework can extend beyond gynecologic and pelvic cancers.
