AN AUTOMATIC COLORECTAL POLYPS DETECTION APPROACH FOR CT COLONOGRAPHY

In this work, we propose an automatic colorectal polyps detection approach that consists of two cascade stages. In the first stage, a CNN model is trained to detect polyps in axial CT slices, The CNN model has been fed by the segmented colon wall CT slices instead of the original CT slices. Using the segmented images as an input to the CNN model has drastically improved the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, the second stage classifier is deployed to exploit the different views of the CT scans instead of the axial view only. So, the classifier is trained using the 2D images of axial views, i.e., the candidate polyps generated by the detector, as well as their corresponding 2D images of sagittal and coronal views. The experimental results of this approach were validated by 3 radiologists and the approach successfully identified polyps after the classification stage with an AUC 98.6%.