Generalizable cone beam CT esophagus segmentation using physics-based data augmentation Journal Article

   


Authors: Alam, S. R.; Li, T.; Zhang, P.; Zhang, S. Y.; Nadeem, S.
Article Title: Generalizable cone beam CT esophagus segmentation using physics-based data augmentation
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 5cc. Due to the physics-based data augmentation, our model trained just on the synthetic CBCTs was robust and generalizable enough to also produce state-of-the-art results on the pCTs and CBCTs, achieving Dice overlaps of 0.81 and 0.74, respectively. It is concluded that our physics-based data augmentation spans the realistic noise/artifact spectrum across patient CBCT/pCT data and can generalize well across modalities, eventually improving the accuracy of treatment setup and response analysis. © 2021 Institute of Physics and Engineering in Medicine.
Keywords: radiotherapy; computerized tomography; image segmentation; semantics; acute esophagitis; automated segmentation; digital storage; response analysis; reconstruction parameters; hausdorff distance; convolutional neural networks; data augmentation; radiation-induced toxicity; adaptive histogram equalization
Journal Title: Physics in Medicine and Biology
Volume: 66
Issue: 6
ISSN: 0031-9155
Publisher: IOP Publishing Ltd  
Date Published: 2021-03-03
Start Page: 065008
Language: English
DOI: 10.1088/1361-6560/abe2eb
PROVIDER: scopus
PUBMED: 33535199
PMCID: PMC8485497
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102480620&doi=10.1088%2f1361-6560%2fabe2eb&partnerID=40&md5=9df478680118401d9b05510a0555ae14
Notes: Article -- Export Date: 1 April 2021 -- Source: Scopus
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MSK Authors
  1. Pengpeng Zhang
    175 Zhang
  2. Tianfang Li
    48 Li
  3. Mohamad Sadegh Riyahi Alam
    49 Riyahi Alam
  4. Saad Nadeem
    50 Nadeem
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