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Cross-Modality Esophagus Segmentation Using Physics-Based Data Augmentation

S Alam1*, T Li2, S Zhang3, D Lee4, P Zhang5, S Nadeem6, (1), (2), (4), (5), (6) Memorial Sloan Kettering Cancer Center, New York, NY, (3) Peking University Cancer Hospital & Institute, Beijing, China

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose:
Automated segmentation of esophagus is critical in image guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis. We developed a semantic physics-based data augmentation method for segmenting esophagus in both planning CT (pCTs) and cone-beam CTs (CBCTs) using 3D convolutional neural networks.

Methods:
The study included 60 lung patients treated via IMRT and had weekly CBCTs. First, week1 CBCTs were deformably registered to their pCTs using regularized diffeomorphic registration. Then, 7 variations of scatter artifacts/noise were extracted from the week1 CBCTs using power-law adaptive histogram equalization that contained the highest to the smoothest frequency components. Extracted CBCT artifacts were added to their corresponding pCT, and 2D projections were generated from the artifact-induced pCTs. The projections were reconstructed using iterative ordered-subset simultaneous algebraic reconstruction technique to generate pseudo CBCTs and were evaluated against their ground-truth CBCT using structure similarity index (SSIM) & root mean square error (RMSE). The pseudo CBCTs were split into 70/30 training (n=294) and testing cases (n=90) and fed to a 3D-UNet for esophagus segmentation using pCT esophagus contours as ground-truth. The model was externally validated on the weekly CBCTs and pCTs using dice coefficient and volume differences between the physician-contoured and UNet-segmented esophagus.

Results:
The best reconstructed pseudo CBCTs had average SSIM=0.89 and RMSE=0.05. Average dice for segmenting pseudo CBCTs, weekly CBCTs, and pCT were 0.74±0.03, 0.71±0.05, and 0.77±0.04, respectively, considerably better than published AAPM segmentation challenge results of 0.72. Average volume differences were 2.6±1.4cc (pseudo-CBCT), 3.4±1.6cc (weekly CBCTs) and 2.3±1.5cc (pCTs).

Conclusion:
3D-UNet model trained on more realistic artifact-induced pCTs, could segment esophagus on both weekly CBCTs and pCTs with high accuracy for longitudinal imaging studies. The model has a potential to segment any OAR on CBCT/pCT and therefore, can be used as a cross-modality segmentation tool to provide image guidance.

Download ePoster [PDF]

Funding Support, Disclosures, and Conflict of Interest: Master research agreement between Memorial Sloan Kettering Cancer Center and Varian

Keywords

Cone-beam CT, Segmentation, Image-guided Therapy

Taxonomy

IM/TH- image Segmentation: Multi-modality segmentation

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