N-Net: Lesion region segmentations using the generalized hybrid dilated convolutions for polyps in colonoscopy images - Cui, Rongsheng; Yang, Runzhuo; Liu, Feng; Cai, Chunqian (Frontiers in Bioengineering and Biotechnology, 2022)
The colorectal cancer is the cancer with the second highest and the third highest incidence rates for the female and the male, respectively. The colorectal polyps are potential prognostic indicators of the colorectal cancer, and the colonoscopy is the gold standard for the biopsy and the removal of the colorectal polyps. In this scenario, one of the main concerns is to ensure the accuracy of the lesion region identifications. However, the missing rate of polyps through manual observations in colonoscopy can reach 14%-30%. In this paper, we focus on the identifications of polyps in clinical colonoscopy images and propose a new N-shaped deep neural network (N-Net) structure to conduct the lesion region segmentations. The encoder-decoder framework is adopted in the N-Net structure and the DenseNet modules are implemented in the encoding path of the network. Moreover, we innovatively propose the generalized hybrid dilated convolution (GHDC) designing strategy, which enables flexible dilated rates and convolutional kernel sizes, to significantly facilitate the transmission of the multi-scale information with the respective fields expanded. Based on GHDC designing strategy, we design four GHDC blocks to connect the encoding and the decoding paths. Through the experiments on two publicly available datasets on polyp segmentations of colonoscopy images: the Kvasir-SEG dataset and the CVC-ClinicDB dataset, the rationality and superiority of the proposed GHDC blocks and the proposed N-Net are verified. Through the comparative studies with the state-of-the-art methods, such as TransU-Net, DeepLabV3+ and CA-Net, we show that even with a small amount of network parameters, the N-Net outperforms significantly with the Dice of 94.45%, the average symmetric surface distance (ASSD) of 0.38 pix and the mean intersection-over-union (mIoU) of 89.80% on the Kvasir-SEG dataset, and with the Dice of 97.03%, the ASSD of 0.16 pix and the mIoU of 94.35% on the CVC-ClinicDB dataset, respectively.