Can Deep Learning Detect Esophageal Lesions In PET-CT Scans?

PET-CT scans using
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F-FDG with a co-registered CT scan are increasingly used to detect cancer. This paper compares deep learning-based lesion detection tools trained on PET, CT and combined modality data. 486 pre-contoured scans were used from a retrospective cohort study into esophageal cancer. Scans were partitioned into training, validation and test sets with an 80:10:10 ratio. 1000 image segments were generated from each scan, with tumor present segments located on the contoured lesion and tumor absent segments distributed randomly within the patient but excluding the tumor. PET and CT image segments were used to train a separate dedicated 5-layer convolutional neural networks (CNN). Testing on segments from unseen scans resulted in an accuracy of greater than 95% for the PET data, and greater than 90% for CT data.