Dual Dimension Reduction and Channel Attention Gate U-Shaped Network for Pancreatic CT Segmentation
Ji Jianbing1, Chen Shu2, Yang Yuanyuan3*
1(College of Information Engineering, Fujian Business University, Fuzhou 350012, China) 2(Faculty of Innovation and Design, City University of Macau, Macau 999078, China) 3(Department General of Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China)
Abstract：Segmentation and reconstruction of pancreatic 3D model from CT images is of great significance for assisting diagnosis of the disease. Due to the small proportion of pancreas and the difficulty to distinguish pancreas from the surrounding tissues, existing methods are not accurate and stable enough. Herein we proposed a dual dimension reduction (DDR) and channel attention gate (CAG) U-shaped network. The DDR was added to the coding path to strengthen the effective information extraction in the shallow feature space, and the CAG was embedded in the skip connection to filter redundant feature information from channel level. On the public data set of pancreas segmentation published by NIH (including 82 CT samples), we evaluated the segmentation performance by dice similarity coefficient (DSC), recall (R) and precision (P), and evaluated 3D reconstruction through vertex distance error (VDE). DSC,R and P indexes reached (82.35±5.76)%, (81.07±8.50)%, and (84.04±5.40)% respectively, and VDE decreased to 1.27±0.90, which was better than that obtained from U-Net, Attention-Unet and other methods in the experiment. The experimental results showed that the method we proposed improved the segmentation performance of pancreatic CT images, and the 3D reconstruction model reflected the actual situation of individual pancreas.
[1] 杨尹默, 田孝东. 中国胰腺癌诊治指南(2021)[J]. 中国实用外科杂志, 2021, 41(7): 725-738. [2] 杨媛媛, 黄鹤光. 三维打印技术在胰腺外科领域的发展现状[J]. 中华外科杂志, 2017, 55(10): 795-797. [3] 周瑞泉, 纪洪辰, 刘荣. 智能医学影像识别研究现状与展望[J]. 第二军医大学学报, 2018, 39(8): 917-922. [4] Zuoyong L, Zhaochai Y, Weixia L, et al. Tongue image segmentation via color decomposition and thresholding[J]. Concurrency and Computation: Practice and Experience, 2019, 31(23):e4662. [5] 徐蔚波, 刘颖, 章浩伟. 基于区域生长的图像分割研究进展[J]. 北京生物医学工程, 2017, 36(3): 317-322. [6] 李翠锦,瞿中. 基于深度学习的图像边缘检测算法综述[J]. 计算机应用, 2020, 40(11): 3280-3288. [7] Karasawa K, Oda M, Kitasaka T, et al. Multi-atlas pancreas segmentation: atlas selection based on vessel structure[J]. Medical Image Analysis, 2017, 39: 18-28. [8] Zheng Q, Delingette H, Duchateau N, et al. 3-D consistent and robust segmentation of cardiac images by deep learning with spatial propagation[J]. IEEE Transactions on Medical Imaging, 2018, 37(9): 2137-2148. [9] 程立英, 高宣爽, 申海, 等. 基于U-Net网络的肺部组织分割[J]. 沈阳师范大学学报(自然科学版), 2020, 38(3): 278-282. [10] 刘蕊, 续欣莹, 谢珺. 基于多维度特征提取网络的肝脏图像分割[J]. 河北大学学报(自然科学版), 2021, 41(4): 426-435. [11] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4): 640-651. [12] 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. Cham: Springer, 2015: 234-241. [13] Zhou Y, Xie L, Shen W, et al. A fixed-point model for pancreas segmentation in abdominal CT scans[C]//International Conference on Medical Image Computing and Compute R-Assisted Intervention. Cham: Springer, 2017: 693-701. [14] Schlemper J, Oktay O, Schaap M, et al. Attention gated networks: Learning to leverage salient regions in medical images[J]. Medical Image Analysis, 2019, 53: 197-207. [15] 魏柳, 向智霆, 刘剑聪, 等. 基于回环残差注意力机制U-net的胰腺分割[J]. 重庆邮电大学学报(自然科学版), 2021, 33(4): 653-660. [16] Roth HR, Lu L, Farag A, et al. DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation[C] //International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 556-564. [17] Liu Y, Zhang F, Zhang Y, et al. Image smoothing based on histogram equalized content-aware patches and direction-constrained sparse gradients[J]. Signal Processing, 2021, 183: 108037. [18] 沈志强, 林超男, 潘林, 等. 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. [19] 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). New York: IEEE, 2016: 565-571. [20] Woo S, Park J, Lee JY, et al. Cbam: convolutional block attention module [EB/OL]. https://arxiv.org/pdf/1807.06521v2.pdf, 2018-07-18/2021-12-16. [21] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016: 770-778. [22] 殷晓航, 王永才, 李德英. 基于U-Net结构改进的医学影像分割技术综述[J]. 软件学报, 2021, 32(2): 519-550. [23] Han XF, Laga H, Bennamoun M. Image-based 3D object reconstruction: State-of-the-art and trends in the deep learning era[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(5): 1578-1604. [24] Shalaby A, Elmogy M, Elfetouh AA. 3D image reconstruction from different image formats using marching cubes technique[J]. International Journal of Computational Vision and Robotics, 2019, 9(3): 293-309. [25] 王硕,王亚飞,李学华.基于多分辨率的快速迭代最近点配准算法[J]. 计算机应用与软件, 2020, 37(4): 260-265,272. [26] Yang YY, Zhao CQ, Wang LS, et al. A novel biopolymer device fabricated by 3D printing for simplifying procedures of pancreatico-jejunostomy[J]. Materials Science & Engineering C-Materials for Biological Applications, 2019, 103: 109786. [27] Erdt M, Kirschner M, Drechsler K, et al. Automatic pancreas segmentation in contrast enhanced CT data using learned spatial anatomy and texture descriptors[C]//2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Chicago: IEEE, 2011: 2076-2082. [28] Farag A, Lu L, Roth HR, et al. A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling[J]. IEEE Transactions on Image Processing, 2016, 26(1): 386-399. [29] You H, Yu L, Tian S, et al. MC-Net: Multiple max-pooling integration module and cross multi-scale deconvolution network[J]. Knowledge-Based Systems, 2021, 231:107456. [30] 戴佳佳,范丽鹏,庞明勇.特征驱动的三维网格模型自适应重采样算法[J]. 系统仿真学报,2019,31(5):853-860.
