  In recent years, deep learning has rapidly become a method of choice for the
segmentation of medical images. Deep Neural Network (DNN) architectures such as
UNet have achieved state-of-the-art results on many medical datasets. To
further improve the performance in the segmentation task, we develop an
ensemble system which combines various deep learning architectures. We propose
a two-layer ensemble of deep learning models for the segmentation of medical
images. The prediction for each training image pixel made by each model in the
first layer is used as the augmented data of the training image for the second
layer of the ensemble. The prediction of the second layer is then combined by
using a weights-based scheme in which each model contributes differently to the
combined result. The weights are found by solving linear regression problems.
Experiments conducted on two popular medical datasets namely CAMUS and
Kvasir-SEG show that the proposed method achieves better results concerning two
performance metrics (Dice Coefficient and Hausdorff distance) compared to some
well-known benchmark algorithms.
