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Gross tumor volume (GTV) segmentation is a critical step in esophageal cancerradiotherapy treatment planning. Inconsistencies across oncologists andprohibitive labor costs motivate automated approaches for this task. However,leading approaches are only applied to radiotherapy computed tomography (RTCT)images taken prior to treatment. This limits the performance as RTCT suffersfrom low contrast between the esophagus, tumor, and surrounding tissues. Inthis paper, we aim to exploit both RTCT and positron emission tomography (PET)imaging modalities to facilitate more accurate GTV segmentation. By utilizingPET, we emulate medical professionals who frequently delineate GTV boundariesthrough observation of the RTCT images obtained after prescribing radiotherapyand PET/CT images acquired earlier for cancer staging. To take advantage ofboth modalities, we present a two-stream chained segmentation approach thateffectively fuses the CT and PET modalities via early and late 3Ddeep-network-based fusion. Furthermore, to effect the fusion and segmentationwe propose a simple yet effective progressive semantically nested network(PSNN) model that outperforms more complicated models. Extensive 5-foldcross-validation on 110 esophageal cancer patients, the largest analysis todate, demonstrates that both the proposed two-stream chained segmentationpipeline and the PSNN model can significantly improve the quantitativeperformance over the previous state-of-the-art work by 11% in absolute Dicescore (DSC) (from 0.654 to 0.764) and, at the same time, reducing the Hausdorffdistance from 129 mm to 47 mm.

Accurate Esophageal Gross Tumor Volume Segmentation in PET/CT using Two-Stream Chained 3D Deep Network Fusion