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Abstract

Dose-volume histogram (DVH) is an important tool to evaluate the radiation treatment plan quality, which could be predicted based on the distance-volume spatial relationship between planning target volumes (PTV) and organs-at-risks (OARs). However, the prediction accuracy is still limited due to the complicated calculation process and the omission of detailed spatial geometric features. In this paper, we propose a spatial geometric-encoding network (SGEN) to incorporate 3D spatial information with an efficient 2D convolutional neural networks (CNN) for accurate prediction of DVH for esophageal radiation treatments. 3D computed tomography (CT) scans, 3D PTV scans and 3D distance images are used as the multi-view input of the proposed model. The dilation convolution based Multi-scale concurrent Spatial and Channel Squeeze & Excitation (msc-SE) structure in the proposed model not only can maintain comprehensive spatial information with less computation cost, but also can extract the features of organs at different scales effectively. Five-fold cross-validation on 200 intensity-modulated radiation therapy (IMRT) esophageal radiation treatment plans were used in this paper. The mean absolute error (MAE) of DVH focusing on the left lung can achieve $2.73 \pm 2.36$ , while the MAE was $7.73 \pm 3.81$ using traditional machine learning prediction model. In addition, extensive ablation studies have been conducted and the quantitative results demonstrate the effectiveness of different components in the proposed method.

Keywords

Dose volume histogram deep learning convolutional neural network feature learning esophageal treatment planning

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Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning

Abstract

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