Deep transfer learning of virtual endoluminal views for the detection of polyps in CT colonography

Proper training of deep convolutional neural networks (DCNNs) requires large annotated image databases that are currently not available in CT colonography (CTC). In this study, we employed a deep transfer learning (DETALE) scheme to circumvent this problem in automated polyp detection for CTC. In our method, a DCNN that had been pre-trained with millions of non-medical images was adapted to identify polyps using virtual endoluminal images of the polyp candidates prompted by a computer-aided detection (CADe) system. For evaluation, 154 CTC cases with and without fecal tagging were divided randomly into a development set and an external validation set including 107 polyps ≥6 mm in size. A CADe system was trained to detect polyp candidates using the development set, and the virtual endoluminal images of the polyp candidates were labeled manually into true-positive and several false-positive (FP) categories for transfer learning of the DCNN. Next, the trained CADe system was used to detect polyp candidates from the external validation set, and the DCNN reviewed their images to determine the final detections. The detection sensitivity of the standalone CADe system was 93% at 6.4 FPs per patient on average, whereas the DCNN reduced the number of FPs to 2.0 per patient without reducing detection sensitivity. Most of the remaining FP detections were caused by untagged stool. In fecal-tagged CTC cases, the detection sensitivity was 94% at only 0.78 FPs per patient on average. These preliminary results indicate that DETALE can yield substantial improvement in the accuracy of automated polyp detection in CTC.