计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 95-100.doi: 10.11896/jsjkx.200700067
徐少伟1,2, 秦品乐1,2, 曾建朝1,2, 赵致楷3, 高媛1,2, 王丽芳1,2
XU Shao-wei1,2, QIN Pin-le1,2, ZENG Jian-chao1,2, ZHAO Zhi-kai3, GAO Yuan1,2, WANG Li-fang1,2
摘要: 针对纵膈淋巴结尺度差异大、正负样本不均衡、软组织和肺肿瘤易混淆的问题,提出一个新颖的用于纵膈淋巴结分割的多级特征和全局上下文分割网络。为了解决纵膈淋巴结正负样本不均衡、与纵膈器官和软组织相似的问题,通过医学先验提取纵膈间隙,减少了纵膈器官干扰。为了解决肿大纵膈淋巴结与肺肿瘤相似、淋巴结出现区域分散的问题,设计了全局上下文模块,通过计算全局上下文依赖,大大提升了网络对淋巴结和背景的分类能力。为了解决纵膈淋巴结尺度差异大的问题,设计了特征融合模块,大大增强了网络对小淋巴结的分割精度。实验表明,所提方法在纵膈淋巴结分割任务中达到了76.92%的准确率,79.65%的召回率和76.08%的dice分数,在准确率、召回率和dice分数上均明显优于当前用于纵膈淋巴结分割的其他算法。
中图分类号:
[1] RUSCH V W,ASAMURA H,WATANABE H,et al.TheIASLC lung cancer staging project:a proposal for a new international lymph node map in the forthcoming seventh edition of the TNM classification for lung cancer[J].Journal of thoracic oncology,2009,4(5):568-577. [2] ZHANG L J.Consensus and controversy of mediastinal lymph node dissection in diagnosis and treatment of lung cancer[J].Chinese Journal of Lung Cancer,2018,21(3):176-179. [3] TERÁN M D,BROCK M V.Staging lymph node metastasesfrom lung cancer in the mediastinum[J].Journal of Thoracic Disease,2014,6(3):230. [4] LaCKEY A,DONINGTON J S.Surgical management of lungcancer[J].Seminars in Interventional Radiology,2013,30(2):133. [5] ODA H,ROTH H R,BHATIA K K,et al.Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images[C]//Medical Imaging 2018:Computer-Aided Diagnosis.International Society for Optics and Photonics,2018:1057502. [6] RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention.Cham:Springer,2015:234-241. [7] BOUGET D,JRGENSEN A,KISS G,et al.Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging[J].International Journal of Computer Assisted Radiology and Surgery,2019,14(2):1-10. [8] CHEN H,WANG X,HUANG Y,et al.Harnessing 2d networksand 3d features for automated pancreas segmentation from volumetric ct images[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2019:339-347. [9] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shotmultibox detector[C]//Computer Vision-ECCV 2016 14th European Conference.2016:21-37. [10] LIN T Y,DOLLAR,PIOTR,et al.Feature Pyramid Networks for Object Detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017).Honolulu,HI,USA,2017:936-944. [11] SHEN W,QIN P,ZENG J.An Indoor crowd detection network framework based on feature aggregation module and hybrid attention selection module[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2019. [12] WANG X,GIRSHICK R,GUPTA A,et al.Non-local NeuralNetworks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7794-7803. [13] CAO Y,XU J,LIN S,et al.Gcnet:Non-local networks meet squeeze-excitation networks and beyond[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2019. [14] SUTSKEVER I,VINYALS O,LE O V.Sequence to Sequence Learning with Neural Networks[C]//Advances in Neural Information Processing Systems 27:Annual Conference on Neural Information Processing Systems 2014.Montreal,Quebec,Canada,2014:3104-3112. [15] HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [16] WANG Z,ZOU N,SHEN D,et al.Non-local U-Nets for Biomedical Image Segmentation[C]//Thirty-Fourth AAAI Confe-rence on Artificial Intelligence.2020. [17] ZHOU Z,SIDDIQUEE M M R,TAJBAKHSH N,et al.Unet++:A nested u-net architecture for medical image segmentation[M]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.Cham:Springer,2018:3-11. [18] ISENSEE F,JÄGER P F,KOHL S A A,et al.Automated design of deep learning methods for biomedical image segmentation[J].arXiv:1904.08128,2019. [19] HE K,GKIOXARI G,DOLLÁR P P,et al.Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2961-2969. [20] CAI Z W,FAN Q F,FERIS R S,et al.A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection[C]//Computer Vision ECCV 2016 14th European Conference.2016:354-370. [21] ZHOU P,NI B B,GENG C.Scale-Transferrable Object Detection[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018).Salt Lake City,UT,USA,2018:528-537. [22] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal Loss for Dense Object Detection[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,PP(99):2999-3007. [23] LI X,WANG W,HU X,et al.Selective kernel networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:510-519. [24] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141. [25] ISENSEE F,JAEGER P,WASSERTHAL J,et al.batchgenerators-a python framework for data augmentation[J].Zenodo.https://doi.org/10.5281/zenodo,2020,3632567. [26] MILLETARI F,NAVAB N,AHMADI S A.V-net:Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision (3DV).IEEE,2016:565-571. |
[1] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
[2] | 张嘉淏, 刘峰, 齐佳音. 一种基于Bottleneck Transformer的轻量级微表情识别架构 Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer 计算机科学, 2022, 49(6A): 370-377. https://doi.org/10.11896/jsjkx.210500023 |
[3] | 赵丹丹, 黄德根, 孟佳娜, 董宇, 张攀. 基于BERT-GRU-ATT模型的中文实体关系分类 Chinese Entity Relations Classification Based on BERT-GRU-ATT 计算机科学, 2022, 49(6): 319-325. https://doi.org/10.11896/jsjkx.210600123 |
[4] | 颜锐, 梁智勇, 李锦涛, 任菲. 基于深度学习和H&E染色病理图像的肿瘤相关指标预测研究综述 Predicting Tumor-related Indicators Based on Deep Learning and H&E Stained Pathological Images:A Survey 计算机科学, 2022, 49(2): 69-82. https://doi.org/10.11896/jsjkx.210900140 |
[5] | 胡艳丽, 童谭骞, 张啸宇, 彭娟. 融入自注意力机制的深度学习情感分析方法 Self-attention-based BGRU and CNN for Sentiment Analysis 计算机科学, 2022, 49(1): 252-258. https://doi.org/10.11896/jsjkx.210600063 |
[6] | 王习, 张凯, 李军辉, 孔芳, 张熠天. 联合自注意力和循环网络的图像标题生成 Generation of Image Caption of Joint Self-attention and Recurrent Neural Network 计算机科学, 2021, 48(4): 157-163. https://doi.org/10.11896/jsjkx.200300146 |
[7] | 周小诗, 张梓葳, 文娟. 基于神经网络机器翻译的自然语言信息隐藏 Natural Language Steganography Based on Neural Machine Translation 计算机科学, 2021, 48(11A): 557-564. https://doi.org/10.11896/jsjkx.210100015 |
[8] | 张佳嘉, 张小洪. 多分支卷积神经网络肺结节分类方法及其可解释性 Multi-branch Convolutional Neural Network for Lung Nodule Classification and Its Interpretability 计算机科学, 2020, 47(9): 129-134. https://doi.org/10.11896/jsjkx.190700203 |
[9] | 张鹏飞, 李冠宇, 贾彩燕. 面向自然语言推理的基于截断高斯距离的自注意力机制 Truncated Gaussian Distance-based Self-attention Mechanism for Natural Language Inference 计算机科学, 2020, 47(4): 178-183. https://doi.org/10.11896/jsjkx.190600149 |
[10] | 康雁,崔国荣,李浩,杨其越,李晋源,王沛尧. 融合自注意力机制和多路金字塔卷积的软件需求聚类算法 Software Requirements Clustering Algorithm Based on Self-attention Mechanism and Multi- channel Pyramid Convolution 计算机科学, 2020, 47(3): 48-53. https://doi.org/10.11896/jsjkx.190700146 |
[11] | 张义杰, 李培峰, 朱巧明. 基于自注意力机制的事件时序关系分类方法 Event Temporal Relation Classification Method Based on Self-attention Mechanism 计算机科学, 2019, 46(8): 244-248. https://doi.org/10.11896/j.issn.1002-137X.2019.08.040 |
[12] | 张雨倩,顾冬云. 帕金森震颤与原发性震颤的计算机辅助诊断方法综述 Review of Computer Aided Diagnosis for Parkinson’s Tremor and Essential Tremor 计算机科学, 2019, 46(7): 22-29. https://doi.org/10.11896/j.issn.1002-137X.2019.07.004 |
[13] | 凡子威, 张民, 李正华. 基于BiLSTM并结合自注意力机制和句法信息的隐式篇章关系分类 BiLSTM-based Implicit Discourse Relation Classification Combining Self-attention Mechanism and Syntactic Information 计算机科学, 2019, 46(5): 214-220. https://doi.org/10.11896/j.issn.1002-137X.2019.05.033 |
[14] | 王帅,刘娟,毕姚姚,陈哲,郑群花,段慧芳. 基于两步聚类和随机森林的乳腺腺管自动识别方法 Automatic Recognition of Breast Gland Based on Two-step Clustering and Random Forest 计算机科学, 2018, 45(3): 247-252. https://doi.org/10.11896/j.issn.1002-137X.2018.03.039 |
[15] | 赵鹏飞,赵涓涓,强彦,王峰智,赵文婷. 多输入卷积神经网络肺结节检测方法研究 Study on Detection Method of Pulmonary Nodules with Multiple Input Convolution Neural Network 计算机科学, 2018, 45(1): 162-166. https://doi.org/10.11896/j.issn.1002-137X.2018.01.028 |
|