3D deep learning for computer-aided detection of serrated polyps in CT colonography

Abstract

Serrated polyps were historically believed to be benign lesions that have no cancer potential. However, recent studies have revealed a molecular pathway where serrated polyps can develop into colorectal cancers. Because serrated polyps tend to be flat and pale lesions, they are challenging to detect in colonoscopy, whereas CT colonography can detect serrated polyps based on a phenomenon called contrast coating. However, the differentiation of contrast coating from tagged feces requires great skill from the reader. The purpose of this pilot study was to explore the performance of 3D deep learning in the detection of serrated polyps. The materials included 94 CT colonography cases with biopsy-confirmed serrated polyps. We explored how to adapt the architecture of our baseline 3D DenseNet into the limited dataset by modification of the architectural parameters. The detection performance of the different 3D DenseNets and a reference 3D ResNet and a 3D AlexNet were compared by use of 10-fold cross-validation in terms of their sensitivity and false-positive rate within a clinically meaningful performance range by use of the free-response operating characteristic analysis. Our preliminary results indicate that the optimized 3D DenseNet can yield a high detection performance for serrated polyps that is comparable to those of state-of-the-art conventional CADe systems for traditional polyps in CT colonography.