结合切片上下文信息的多阶段胰腺定位与分割

doi:10.12263/DZXB.20200101

电子学报 ›› 2021, Vol. 49 ›› Issue (4): 706-715.DOI: 10.12263/DZXB.20200101

结合切片上下文信息的多阶段胰腺定位与分割

王瑞豪, 刘哲, 宋余庆

江苏大学计算机科学与通信工程学院, 江苏镇江 212013

收稿日期:2020-01-15 修回日期:2020-06-30 出版日期:2021-04-25
- 通讯作者:
- 刘哲
- 作者简介:
- 王瑞豪 男,1995年4月出生,安徽阜阳人.现为江苏大学计算机科学与通信工程学院硕士研究生,主要研究方向为医学图像处理与分析.E-mail:2221808037@stmail.ujs.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61976106，No.61772242，No.61572239）; 国家博士后科研基金 (No.2017M611737）; 江苏省六大人才高峰 (No.DZXX-122）; 镇江市卫生计生科技重点 (No.SHW2017019）

Multi-Stage Pancreas Localization and Segmentation Combined with Slices Context Information

WANG Rui-hao, LIU Zhe, SONG Yu-qing

School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China

Received:2020-01-15 Revised:2020-06-30 Online:2021-04-25 Published:2021-04-25

摘要/Abstract

摘要： 当前基于深度学习的胰腺分割主要存在以下问题：（1）胰腺的解剖特异性导致深度网络模型容易受到复杂多变背景的干扰；（2）传统两阶段分割方法在粗分割阶段将整张CT图像作为输入，导致依赖粗分割结果得到的定位不够准确；（3）传统两阶段分割方法忽略了切片间的上下文信息，限制了定位和后续分割结果的提升.针对上述问题，本文提出了结合切片上下文信息的多阶段胰腺定位与分割方法.第一阶段利用解剖先验定位粗略缩小输入区域；第二阶段先使用所设计的DASU-Net进行粗略分割，接着利用切片上下文信息优化分割结果；第三阶段使用单张切片定位进一步减少不相关背景，并使用DASU-Net完成精细分割.实验结果表明，本文所提方法能够有效提高胰腺分割的准确率.

关键词: 胰腺分割, 多阶段分割, 切片上下文信息, 解剖先验定位, 单张切片定位

Abstract: Current deep learning-based pancreas segmentation mainly has the following problems:The anatomical specificity of the pancreas makes the deep network model easily disturbed by complex background;in the traditional two-stage segmentation method,the input of the coarse segmentation is the entire CT image,which leads to inaccurate localization based on the segmentation results;the traditional two-stage segmentation ignores the context information between adjacent slices,which limits the localization and subsequent segmentation results.In order to solve the problems above,a multi-stage pancreas localization and segmentation method combined with slices context information is proposed.In the first stage,anatomical prior locating is used to roughly shrink the input area;in the second stage,the proposed DASU-Net is used for coarse segmentation,and then the segmentation results are optimized with slices context information;last stage,single slice locating is used to further shrink irrelevant background,and then fine segmentation is completed by DASU-Net.The experimental results show that the proposed method can effectively improve the accuracy of pancreas segmentation.

Key words: pancreas segmentation, multi-stage segmentation, slices context information, anatomical prior locating, single slice locating

中图分类号:

TP391

王瑞豪, 刘哲, 宋余庆. 结合切片上下文信息的多阶段胰腺定位与分割[J]. 电子学报, 2021, 49(4): 706-715.

WANG Rui-hao, LIU Zhe, SONG Yu-qing. Multi-Stage Pancreas Localization and Segmentation Combined with Slices Context Information[J]. Acta Electronica Sinica, 2021, 49(4): 706-715.

参考文献

[1] 范虹,张程程,侯存存,等.结合双树复小波变换和改进密度峰值快速搜索聚类的乳腺MR图像分割[J].电子学报,2019,47(10):2149-2157. FAN Hong,ZHANG Cheng-cheng,HOU Cun-cun,et al.Dual-tree complex wavelet transform and improved density peak fast search and clustering method for breast MR image segmentation[J].Acta Electronica Sinica,2019,47(10):2149-2157.(in Chinese)
[2] 姜慧研,冯锐杰.基于改进的变分水平集和区域生长的图像分割方法的研究[J].电子学报,2012,40(8):1659-1664. JIANG Hui-yan,FENG Rui-jie.Image segmentation method research based on improved variational level set and region growth[J].Acta Electronica Sinica,2012,40(8):1659-1664.(in Chinese)
[3] 张建伟,方林,陈允杰,等.基于活动轮廓模型的左心室MR图像分割[J].电子学报,2011,39(11):2670-2673. ZHANG Jian-wei,FANG Lin,CHEN Yun-jie,et al.Left ventricle MRI segmentation based on active contour model[J].Acta Electronica Sinica,2011,39(11):2670-2673.(in Chinese)
[4] 葛婷,牟宁,李黎.基于softmax回归与图割法的脑肿瘤分割算法[J].电子学报,2017,45(3):644-649. GE Ting,MU Ning,LI Li.A brain tumor segmentation method based on softmax regression and graph cut[J].Acta Electronica Sinica,2017,45(3):644-649.(in Chinese)
[5] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[A].Proceedings of IEEE Computer Vision and Pattern Recognition[C].Boston:IEEE,2015.3431-3440.
[6] ZHOU Y,XIE L,SHEN W,et al.A fixed-point model for pancreas segmentation in abdominal CT scans[A].International Conference on Medical Image Computing and Computer-Assisted Intervention[C].Quebec:Springer,2017.693-701.
[7] CHEN J,YANG L,ZHANG Y,et al.Combining fully convolutional and recurrent neural networks for 3D biomedical image segmentation[A].Advances in Neural Information Processing Systems[C].Barcelona:MIT Press,2016.3036-3044.
[8] CAI J,LU L,XING F,et al.Pancreas segmentation in CT and MRI via task-specific network design and recurrent neural contextual learning[A].Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics[C].Switzerland:Springer,2019.3-21.
[9] LI H,LI J,LIN X,et al.Pancreas segmentation via spatial context based U-Net and bidirectional LSTM[DB/OL].Arxiv:1903.00832v1,2019-03-03/2020-01-06.
[10] OKADA T,LINGURARU M G,HORIM,et al.Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors[A].International Conference on Medical Image Computing and Computer-Assisted Intervention[C].Nagoya:Springer,2013.275-282.
[11] RONNEBERGER O,FISCHER P,BROX T.U-Net:convolutional networks for biomedical image segmentation[A].International Conference on Medical Image Computing and Computer-Assisted Intervention[C].Munich:Springer,2015.234-241.
[12] YU F,WANG D,SHELHAMER E,et al.Deep layer aggregation[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].Salt Lake City:IEEE Computer Society,2018.2403-2412.
[13] LEE C,XIE S,GALLAGHER P W,et al.Deeply-supervised nets[A].Proceedings of the International Conference on Artificial Intelligence and Statistics[C].San Diego:MLR Press,2015.562-570.
[14] SHI X,CHEN Z,WANG H,et al.Convolutional lstm network:a machine learning approach for precipitation nowcasting[A].International Conference on Neural Information Processing Systems[C].Montreal:JMLR,2015.802-810.
[15] ROTH H R,LU L,FARAG A,et al.Deeporgan:multi-level deep convolutional networks for automated pancreas segmentation[A].International Conference on Medical Image Computing and Computer-Assisted Intervention[C].Munich:Springer,2015.556-564.
[16] 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.
[17] ZHOU Z,RAHMAN Siddiquee M M,TAJBAKHSH N,et al.UNet++:A nested u-net architecture for medical image segmentation[A].Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support[C].Spain:Springer,2018.3-11.
[18] ROTH H R,LU L,FARAG A,et al.Spatial aggregation of holistically-nested networks for automated pancreas segmentation[A].International Conference on Medical Image Computing and Computer-Assisted Intervention[C].Athens:Springer,2016.451-459.
[19] CAI J,LU L,XIE Y,et al.Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function[A].International Conference on Medical Image Computing and Computer-Assisted Intervention[C].Quebec:Springer,2017.674-682.
[20] ROTH HR,LU L,LAY N,et al.Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation[J].Medical Image Analysis,2018,45:94-107.
[21] LI J,LIN X,CHE H,et al.Probability map guided bi-directional recurrent unet for pancreas segmentation[DB/OL].Arxiv:1903.00923v3,2019-03-03/2020-01-15.
[22] ASATURYAN H,GLIGORIEVSKI A,VILLARINI B.Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation[J].Computerized Medical Imaging and Graphics,2019,75:1-13.

结合切片上下文信息的多阶段胰腺定位与分割

Multi-Stage Pancreas Localization and Segmentation Combined with Slices Context Information

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	王子为, 鲁继文, 周杰. 基于自适应梯度优化的二值神经网络[J]. 电子学报, 2023, (): 1-10.
[2]	刘金平, 吴娟娟, 张荣, 徐鹏飞. 基于结构重参数化与多尺度深度监督的COVID-19胸部CT图像自动分割[J]. 电子学报, 2023, (): 1-9.
[3]	张笑宇, 沈超, 蔺琛皓, 李前, 王骞, 李琦, 管晓宏. 面向机器学习模型安全的测试与修复[J]. 电子学报, 2023, (): 1-35.
[4]	王炼红, 罗志辉, 林飞鹏, 李潇瑶. 采用多头注意力机制的C&RM-MAKT预测算法[J]. 电子学报, 2022, (): 1-9.
[5]	苏田田, 王慧敏, 张小凤. 基于多分支瓶颈结构的轻量型图像分类算法研究[J]. 电子学报, 2022, (): 1-9.
[6]	刘芳, 朱天贺, 苏卫星, 刘阳. 基于高斯隐马尔可夫模型的人机共享控制区域化决策算法[J]. 电子学报, 2022, 50(11): 2659-2667.
[7]	桑海峰, 陈旺兴, 王海峰, 王金玉. 基于多模式时空交互的行人轨迹预测模型[J]. 电子学报, 2022, 50(11): 2806-2812.
[8]	刘耿耿, 李泽鹏, 郭文忠, 陈国龙, 徐宁. 面向超大规模集成电路物理设计的通孔感知的并行层分配算法[J]. 电子学报, 2022, 50(11): 2575-2583.
[9]	魏博文, 全红艳. 基于语义与形态特征融合的语义分割网络[J]. 电子学报, 2022, 50(11): 2688-2697.
[10]	姚睿, 朱享彬, 周勇, 王鹏, 张艳宁, 赵佳琦. 基于重要特征的视觉目标跟踪可迁移黑盒攻击方法[J]. 电子学报, 2022, (): 1-10.
[11]	金紫凤, 潘思聪, 危辉. 可变环境下基于位姿变换矩阵的机器人无标定手眼协调方法[J]. 电子学报, 2022, 50(10): 2318-2328.
[12]	魏钰轩, 陈莹. 基于自适应层信息熵的卷积神经网络压缩[J]. 电子学报, 2022, 50(10): 2398-2408.
[13]	马百腾, 张士伟, 高常鑫, 桑农. 面向行为边界框生成的端到端时间全局相关网络[J]. 电子学报, 2022, 50(10): 2452-2461.
[14]	肖斌, 陈嘉博, 毕秀丽, 张俊辉, 李伟生, 王国胤, 马旭. 基于一维卷积神经网络与循环神经网络串联的心音分析方法[J]. 电子学报, 2022, 50(10): 2425-2432.
[15]	周登文, 李文斌, 李金新, 黄志勇. 一种轻量级的多尺度通道注意图像超分辨率重建网络[J]. 电子学报, 2022, 50(10): 2336-2346.