Generalizable Cone Beam CT Esophagus Segmentation Using In Silico Data Augmentation

Lung cancer radiotherapy entails high quality planning computed tomography (pCT) imaging of the patient with radiation oncologist contouring of the tumor and the organs at risk (OARs) at the start of the treatment. This is followed by weekly low-quality cone beam CT (CBCT) imaging for treatment setup and qualitative visual assessment of tumor and critical OARs. In this work, we aim to make the weekly CBCT assessment quantitative by automatically segmenting the most critical OAR, esophagus, using deep learning and in silico (image-driven simulation) artifact induction to convert pCTs to pseudo-CBCTs (pCTs$+$artifacts). Specifically, for the in silico data augmentation, we make use of the critical insight that CT and CBCT have the same underlying physics and that it is easier to deteriorate the pCT to look more like CBCT (and use the accompanying high quality manual contours for segmentation) than to synthesize CT from CBCT where the critical anatomical information may have already been lost (which leads to anatomical hallucination with the prevalent generative adversarial networks for example). Given these pseudo-CBCTs and the high quality manual contours, we introduce a modified 3D-Unet architecture and a multi-objective loss function specifically designed for segmenting soft-tissue organs such as esophagus on real weekly CBCTs. The model achieved 0.74 dice overlap (against manual contours of an experienced radiation oncologist) on weekly CBCTs and was robust and generalizable enough to also produce state-of-the-art results on pCTs, achieving 0.77 dice overlap against the previous best of 0.72. This shows that our in silico data augmentation spans the realistic noise/artifact spectrum across patient CBCT/pCT data and can generalize well across modalities (without requiring retraining or domain adaptation), eventually improving the accuracy of treatment setup and response analysis.