Abstract：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.
沈志强, 林超男, 潘林, 聂炜宇, 裴玥, 黄立勤, 郑绍华. 基于同构化改进的U-Net结直肠息肉分割方法[J]. 中国生物医学工程学报, 2022, 41(1): 48-56.
Shen Zhiqiang, Lin Chaonan, Pan Lin, Nie Weiyu, Pei Yue, Huang Liqin, Zheng Shaohua. A Colorectal Segmentation Method Based on U-Net Improved with Identical Design. Chinese Journal of Biomedical Engineering, 2022, 41(1): 48-56.
[1] Sung H, Ferlay J, Siegel R, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249. [2] Siegel RL, Miller KD, Goding Sauer A, et al. Colorectal cancer statistics, 2020 [J]. CA: A Cancer Journal for Clinicians, 2020, 70(3): 145-164. [3] Shussman N, Wexner SD. Colorectal polyps and polyposis syndromes [J]. Gastroenterology Report, 2014, 2(1): 1-15. [4] Leufkens AM, Van M, Vleggaar FP, et al. Factors influencing the miss rate of polyps in a back-to-back colonoscopy study [J]. Endoscopy, 2012, 44(5): 470-475. [5] Prasath VB. Polyp detection and segmentation from video capsule endoscopy: a review [J]. Journal of Imaging, 2017, 3: 1. [6] Yao J, Miller M, Franaszek M, et al. Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models [J]. IEEE Transactions on Medical Imaging, 2004, 23(11): 1344-1352. [7] Sanchez-Gonzalez A, Garcia-Zapirain B, Sierra-Sosa D, et al. Automatized colon polyp segmentation via contour region analysis [J]. Computers in Biology and Medicine, 2018, 100: 152-164. [8] Yuan Yixuan, Li Dengwang, Meng M. Automatic polyp detection via a novel unified bottom-up and top-down saliency approach [J]. IEEE Journal of Biomedical and Health Informatics, 2017, 22(4): 1250-1260. [9] Van C, Van VF, Vos FM, et al. Detection and segmentation of colonic polyps on implicit isosurfaces by second principal curvature flow [J]. IEEE Transactions on Medical Imaging, 2010, 29(3): 688-698. [10] Huang Gao, Liu Zhuang, Van L, et al. Densely connected convolutional networks [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 4700-4708. [11] He Kaiming, Zhang Xiangyu, Ren Shaoqun, et al. Deep residual learning for image recognition [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas : IEEE, 2016: 770-778. [12] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 3431-3440. [13] Ren Shaoqun, He Kaiming, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks [C] //Advances in Neural Information Processing Systems. Montréal: NeurIPS, 2015, 28: 91-99. [14] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation [C] //International Conference on Medical Image Computing and Computer-assisted Intervention. Munich: Springer, 2015: 234-241. [15] Zhou Zongwei, Siddiquee M, Tajbakhsh N, et al. Unet++: a nested u-net architecture for medical image segmentation [C] //Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer, 2018: 3-11. [16] Zhou Zongwei, Siddiquee M, Tajbakhsh N, et al. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation [J]. IEEE Transactions on Medical Imaging, 2019, 39(6): 1856-1867. [17] Ibtehaz N, Rahman MS. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation [J]. Neural Networks, 2020, 121: 74-87. [18] Bernal J, Sánchez FJ, Fernández-Esparrach G, et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians [J]. Computerized Medical Imaging and Graphics, 2015, 43: 99-111. [19] Fernández-Esparrach G, Bernal J, López-Cerón M, et al. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps [J]. Endoscopy, 2016, 48(9): 837-842. [20] Milletari F, Navab N, Ahmadi SA. V-net: fully convolutional neural networks for volumetric medical image segmentation [C]//2016 Fourth International Conference on 3D Vision (3DV). Stanford: IEEE, 2016: 565-571. [21] Lin T, Goyal P, Girshick R, et al. Focal loss for dense object detection [C] //Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2980-2988. [22] Paszke A, Gross S, Massa F, et al. Pytorch: An imperative style, high-performance deep learning library [C] //Advances in Neural Information Processing Systems. Vancouver: NeurIPS, 2019, 32: 8026-8037. [23] Yu F, Wang D, Shelhamer E, et al. Deep layer aggregation [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2403-2412. [24] Bi L, Kim J, Ahn E, et al. Step-wise integration of deep class-specific learning for dermoscopic image segmentation [J]. Pattern Recognition, 2019, 85: 78-89. [25] Tang Peng, Liang Qiaokang, Yan Xintong, et al. Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging [J]. Computer Methods and Programs in Biomedicine, 2019, 178: 289-301. [26] Zheng S, Nie W, Pan L, et al. A dual-attention V-network for pulmonary lobe segmentation in CT scans [J]. IET Image Processing, 2021, 15(8): 1644-1654.
