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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Multi-model study of fast VMAT segment dose calculation with deep learning.

Fan Xiao¹, Niklas Wahl^2,3, Claus Belka^1,4,5

¹Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.

Physics in Medicine and Biology

|April 23, 2026

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This summary is machine-generated.

Deep learning models for photon dose calculation in radiation therapy were evaluated. Lightweight models using Beam's Eye View (BEV) achieved accurate and fast dose calculations, outperforming patient-coordinate methods.

Area of Science:

Medical Physics
Radiotherapy
Computational Imaging

Background:

Deep learning (DL) enables photon dose calculation using Beam's Eye View (BEV) or patient coordinates.
Evaluating DL model accuracy and speed across coordinate systems is crucial for clinical implementation.

Purpose of the Study:

To compare the dose calculation accuracy and speed of various DL models under BEV and patient coordinate systems.
To introduce lightweight DL models for efficient photon dose calculation.

Main Methods:

Utilized CT scans and VMAT plans from 24 prostate cancer patients.
Trained and tested five DL models (CNN-ConvLSTM, CNN-Mamba, DoTA, C3D, DeepDose-C3D) using Monte Carlo simulations for dose data.
Assessed accuracy via gamma passing rates (γPR) and dose-volume histogram metrics; measured dose calculation times on GPUs.

Keywords:

VMAT deep learning dose calculation photon therapy

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Main Results:

All models achieved high accuracy (≥91.0% for segment doses at 2%/3 mm, ≥99.0% for plan doses at 1%/3 mm).
Lightweight BEV models (CNN-ConvLSTM, CNN-Mamba) demonstrated significantly faster calculation times (e.g., 5.5s and 6.2s per plan) compared to patient-coordinate models.
BEV-based models showed more robust segment performance.

Conclusions:

Both BEV and patient-coordinate DL methods provide accurate photon dose calculations.
BEV approaches offer more robust segment dose prediction.
CNN-ConvLSTM and CNN-Mamba are promising lightweight DL models for fast and accurate photon dose calculation in radiotherapy.