Polyp detection and segmentation in colonoscopy images plays an important role in early detection of colorectal cancer. The paper describes methodology adopted for the EndoVisSub2017/2018 Gastrointestinal Image ANAlysis -(GIANA) polyp segmentation sub-challenges. The developed segmentation algorithms are based on the fully convolutional neural network (FCNN) model. Two novel variants of the FCNN have been investigated, implemented and evaluated. The first one, combines the deep residual network and the dilation kernel layers within the fully convolutional network framework. The second proposed architecture is based on the U-net network augmented by the dilation kernels and "squeeze and extraction" units. The proposed architectures have been evaluated against the well-known FCN8 model. The paper describes the adopted evaluation metrics and presents the results on the GIANA dataset. The proposed methods produced competitive results, securing the first place for the SD and HD image segmentation tasks at the 2017 GIANA challenge and the second place for the SD images at the 2018 GIANA challenge.
Guo et al. REF presented two variants of fully convolutional neural networks, which secured the first position at the 2017 Gastrointestinal Image ANAlysis (GIANA) challenge and second position at the 2018 GIANA challenge.
GIANA Polyp Segmentation with Fully Convolutional Dilation Neural Networks
VISIGRAPP
Computer Science