ArticlePhysics in medicine and biology2021
Generalizable cone beam CT esophagus segmentation using physics-based data augmentation.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).La Radiologia medica · 2024Pooled it
- Contrastive adversarial mix-up learning for unsupervised cross-modality image segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Deformable Image Registration Uncertainty-Encompassing Dose Accumulation for Adaptive Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Creating patient-specific digital phantoms with a longitudinal atlas for evaluating deformable CT-CBCT registration in adaptive lung radiotherapy.Medical physics · 2024Article
- RMSim: controlled respiratory motion simulation on static patient scans.Physics in medicine and biology · 2023Article
- Fully automated condyle segmentation using 3D convolutional neural networks.Scientific reports · 2022Article
- A Proof-of-Concept Study of Artificial Intelligence-assisted Contour Editing.Radiology. Artificial intelligence · 2022Article
- Multitask 3D CBCT-to-CT translation and organs-at-risk segmentation using physics-based data augmentation.Medical physics · 2021Article
- Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy.International journal of radiation oncology, biology, physics · 2021Article
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Authors and funding
5 authors.
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Abstract
Automated segmentation of the esophagus is critical in image-guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis. We have developed a semantic physics-based data augmentation method for segmenting the esophagus in both planning CT (pCT) and cone beam CT (CBCT) using 3D convolutional neural networks. One hundred and ninety-one cases with their pCTs and CBCTs from four independent datasets were used to train a modified 3D U-Net architecture and a multi-objective loss function specifically designed for soft-tissue organs such as the esophagus. Scatter artifacts and noises were extracted from week-1 CBCTs using a power-law adaptive histogram equalization method and induced to the corresponding pCT were reconstructed using CBCT reconstruction parameters. Moreover, we leveraged physics-based artifact induction in pCTs to drive the esophagus segmentation in real weekly CBCTs. Segmentations were evaluated using the geometric Dice coefficient and Hausdorff distance as well as dosimetrically using mean esophagus dose and D
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