An Improved Framework Called Du++ Applied to Brain Tumor Segmentation
Fujuan Chen, Yi Ding, Zhixing Wu, Dongyuan Wu, Jin-Mei Wen · 2018
We all know the advanced framework which is used to medical image processing is Unet, but it is struggling when it processes complex images. DenseNet is the state-of-the-art network, which has large parameters compared with Unet. Unet++ performs better on complex images than Unet. In this work, we proposes an novel network structure called Dense_Unet++(DU++), that can take advantage of feature fusion of the Unet++, reduces the DenseNet's parameters and further improves the segmentation accuracy. Our model is mainly implemented by combine Half Dense Unet(HDU)and Unet++. The long connections with different semantic levels do not achieve the effect of feature fusion, so our paper propose that built a series of bridges for different semantic levels within the DU++ and abandoned the original long connections. We apply this framework to brain tumor segmentation. In the end, our experiment achieved a promising result.