  Automatic polyp detection and segmentation are highly desirable for colon
screening due to polyp miss rate by physicians during colonoscopy, which is
about 25%. However, this computerization is still an unsolved problem due to
various polyp-like structures in the colon and high interclass polyp variations
in terms of size, color, shape, and texture. In this paper, we adapt Mask R-CNN
and evaluate its performance with different modern convolutional neural
networks (CNN) as its feature extractor for polyp detection and segmentation.
We investigate the performance improvement of each feature extractor by adding
extra polyp images to the training dataset to answer whether we need deeper and
more complex CNNs or better dataset for training in automatic polyp detection
and segmentation. Finally, we propose an ensemble method for further
performance improvement. We evaluate the performance on the 2015 MICCAI polyp
detection dataset. The best results achieved are 72.59% recall, 80% precision,
70.42% dice, and 61.24% Jaccard. The model achieved state-of-the-art
segmentation performance.
