基于同构化改进的U-Net结直肠息肉分割方法

结肠镜检查广泛应用于结直肠癌的早期筛查和诊疗,但仅靠人工判读结肠息肉漏检率较高,有研究统计可达25％.基于深度学习的计算机辅助技术有助于提高息肉检测率,但目前深度学习的主流分割网络U-Net存在着两个局限:一是编解码的输出特征图之间存在着语义鸿沟;二是U-Net的双层卷积单元无法学习多尺度信息;割裂地看待容易使模型陷入...

A Colorectal Segmentation Method Based on U-Net Improved with Identical Design

Colonoscopy is a widely used technique for colon screening and polyp lesions diagnosis. Nevertheless, manual screening using colonoscopy suffers from a miss rate around 25% of polyps. Deep learning-based computer-aided diagnosis (CAD) for polyp detection has potentials of reducing the human errors. Polyp detection depends on encoder-decoder network (U-Net) for polyp segmentation. However, U-Net has two limitations, one is that the semantic gap exists between the feature maps from the encoder and decoder; the other one is convolutional layers in the encoder-decoder processing units fail to extract multi-scale information. In this work, we proposed an identical network (I-Net) to tackle the problems in a consolidated manner. The I-Net introduced identical units (IU) both in skip connections and encoder-decoder sub-networks of U-Net to reduce the semantic gap. Meanwhile, motivated by the dense and residual connections, we designed a dense residual unit (DRU) to learn multi-scale information. Finally, DRI-Net was developed by initializing IU to DRU, which not only alleviated the semantic gap between the encoder and the decoder but also learned multi-scale features. We evaluated the proposed methods on the CVC-ClinicDB dataset containing 612 colonoscopy images through five-fold cross validation. Experimental results demonstrated that the DRI-Net achieved Dice coefficient of 90.06% and intersection over union (IoU) of 85.52%. Compared to the U-Net, DRI-Net improved the Dice coefficient of 8.50% and IoU of 11.03%. In addition, we studied the generalization of the proposed methods on International Skin Imaging Collaboration (ISIC) 2017 dataset including a training set of 2 000 dermoscopy images for model training and a test set of 600 images for model evaluation. The study indicated that the I-Net achieved Dice coefficient of 86.57% and IoU of 79.20%. Compared to the first-place solution on ISIC 2017 leaderboard, the DRI-Net improved Dice coefficient of 1.67% and IoU of 2.70%. In conclusion, the results demonstrated that DRI-Net effectively overcome the limitations of U-Net and improved the segmentation accuracy in the polyp segmentation task, and showed the great generalization capability on other modality data.