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Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk

Authors :
Jin, Dakai
Guo, Dazhou
Ho, Tsung-Ying
Harrison, Adam P.
Xiao, Jing
Tseng, Chen-kan
Lu, Le
Publication Year :
2019

Abstract

Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT). High intra- and inter-user variability makes this a particularly difficult task for esophageal cancer. This motivates automated solutions, which is the aim of our work. Because CTV delineation is highly context-dependent--it must encompass the GTV and regional lymph nodes (LNs) while also avoiding excessive exposure to the organs at risk (OARs)--we formulate it as a deep contextual appearance-based problem using encoded spatial contexts of these anatomical structures. This allows the deep network to better learn from and emulate the margin- and appearance-based delineation performed by human physicians. Additionally, we develop domain-specific data augmentation to inject robustness to our system. Finally, we show that a simple 3D progressive holistically nested network (PHNN), which avoids computationally heavy decoding paths while still aggregating features at different levels of context, can outperform more complicated networks. Cross-validated experiments on a dataset of 135 esophageal cancer patients demonstrate that our encoded spatial context approach can produce concrete performance improvements, with an average Dice score of 83.9% and an average surface distance of 4.2 mm, representing improvements of 3.8% and 2.4 mm, respectively, over the state-of-the-art approach.<br />Comment: MICCAI 2019 (early accept)

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1909.01526
Document Type :
Working Paper