  Colorectal cancer (CRC) is the first cause of death in many countries. CRC
originates from a small clump of cells on the lining of the colon called
polyps, which over time might grow and become malignant. Early detection and
removal of polyps are therefore necessary for the prevention of colon cancer.
In this paper, we introduce an ensemble of medical polyp segmentation
algorithms. Based on an observation that different segmentation algorithms will
perform well on different subsets of examples because of the nature and size of
training sets they have been exposed to and because of method-intrinsic
factors, we propose to measure the confidence in the prediction of each
algorithm and then use an associate threshold to determine whether the
confidence is acceptable or not. An algorithm is selected for the ensemble if
the confidence is below its associate threshold. The optimal threshold for each
segmentation algorithm is found by using Comprehensive Learning Particle Swarm
Optimization (CLPSO), a swarm intelligence algorithm. The Dice coefficient, a
popular performance metric for image segmentation, is used as the fitness
criteria. Experimental results on two polyp segmentation datasets MICCAI2015
and Kvasir-SEG confirm that our ensemble achieves better results compared to
some well-known segmentation algorithms.
