计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 12-16.doi: 10.11896/jsjkx.210700217

• 智慧医疗 • 上一篇    下一篇

基于多尺度特征的脑肿瘤分割算法

孙福权1, 崔志清1,2, 邹彭1,2, 张琨1   

  1. 1 东北大学秦皇岛分校数学与统计学院 河北 秦皇岛 066000
    2 东北大学信息科学与工程学院 沈阳 110000
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 崔志清(1971789@stu.neu.edu.cn)
  • 作者简介:(sunfq@neuq.edu.cn)
  • 基金资助:
    国家重点研发计划项目(2018YFB1402800);河北省高教研究与实践项目(2018GJJG422)

Brain Tumor Segmentation Algorithm Based on Multi-scale Features

SUN Fu-quan1, CUI Zhi-qing1,2, ZOU Peng1,2, ZHANG Kun1   

  1. 1 School of Mathematics and Statistics,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066000,China
    2 School of Information Science and Engineering,Northeastern University,Shenyang 110000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:SUN Fu-quan,born in 1964,Ph.D,postdoctoral fellow,professor.His main research interests include medical image processing and big data analysis.
    CUI Zhi-qing,born in 1997,postgra-duate.Her main research interests include image processing and computer vision.
  • Supported by:
    National Key R & D Program of China(2018YFB1402800) and Hebei Higher Education Research Practice Project(2018GJJG422).

摘要: 脑肿瘤是除脑血管病外神经系统最常见的疾病,其分割也是医学图像处理领域的一个重要方向。准确地分割出肿瘤区域是治疗脑肿瘤的首要步骤。针对传统的全卷积神经网络多尺度处理能力弱而造成信息丢失的问题,提出了一种基于多尺度特征的全卷积神经网络用于脑肿瘤区域分割。利用空间金字塔池化可以获得多感受野的高级特征,从而捕获上下文多尺度信息,提高模型对不同尺度特征的适应能力;用残差紧密模块代替原有卷积层,可以缓解训练深度网络时的退化问题,提取更多的特征;结合数据增强技术,避免过拟合的同时最大程度地强化了模型的分割性能。在公开的低级神经胶质瘤核磁共振成像数据集上进行大量对比消融实验分析,以Dice系数、Jaccard指数和准确性作为分割性能的主要评价标准,获得了91.8%的Dice系数、85.0%的Jaccard指数和99.5%的准确性。实验结果表明,该方法能有效分割出脑肿瘤区域并具有一定的泛化性,且相比其他网络相比分割效果更好。

关键词: 多尺度特征, 核磁共振成像, 脑肿瘤分割, 全卷积神经网络, 深度学习

Abstract: Brain tumors are the most common diseases of nervous system except cerebrovascular disease,and their segmentation is also an important field in medical image processing.Accurately segmenting the tumor region is the first step in the treatment of brain tumors.Aiming at the problem of information loss caused by the weak multi-scale processing ability of traditional fully convolutional networks,a fully convolutional network based on multi-scale features is proposed.Using spatial pyramid pooling to obtain advanced features of multiple receptive fields,thereby capturing contextual multi-scale information and improving the adaptability to different scale features.Replacing the original convolution layer with the residual compact module can alleviate the degradation problem and extract more features.The data augmentation technology is combined to enhance the segmentation perfor-mance maximally while avoiding over fitting.Through a large number of contrastive ablation experiments on the public low-grade glioma MRI dataset,using Dice coefficient,Jaccard index and accuracy as the main evaluation criteria,91.8% Dice coefficient,85.0% Jaccard index and 99.5% accuracy are obtained.Experimental results show that the proposed method can effectively segment brain tumor regions and have certain generalization,and the segmentation effect is better than other networks.

Key words: Brain tumor segmentation, Deep learning, Fully convolutional network, Magnetic resonance imaging, Multi-scale features

中图分类号: 

  • TP391
[1] AHIR B K,ENGELHARD H H,LAKKA S S.Tumor development and angiogenesis in adult brain tumor:Glioblastoma[J].Molecular Neurobiology,2020,57(5):2461-2478.
[2] TABATABAI G,STUPP R,BENT M,et al.Molecular diagnostics of gliomas:the clinical perspective[J].Acta Neuropathologica,2010,120(5):585-592.
[3] GORDILLO N,MONTSENY E,SOBREVILLA P.State of the art survey on MRI brain tumor segmentation[J].Magnetic Reso-nance Imaging,2013,31(8):1426-1438.
[4] NEMA S,DUDHANE A,MURALA S,et al.RescueNet:Anunpaired GAN for brain tumor segmentation[J].Biomedical Signal Processing and Control,2020,55:101641.
[5] MENZE B H,JAKAB A,BAUER S,et al.The multimodal brain tumor image segmentation benchmark(BRATS)[J].IEEE Transactions on Medical Imaging,2015,34(10):1993-2024.
[6] MOESKOPS P,BENDERS M J,CHIT S M,et al.Automatic segmentation of MR brain images of preterm infants using supervised classification[J].Neuroimage,2015,118:628-641.
[7] XU H X,CAO W H,CHEN W,et al.Segmentation of multi-target image based on SVM[J].Microelectronics & Computer,2009,26(04):5-10.
[8] ASHBURNER J,FRISTON K J.Unified segmentation[J].Neuroimage,2005,26(3):839-851.
[9] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:3431-3440.
[10] RONNEBERGER O,FISCHER P,BROX T.U-net:convolu-tional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Compu-ter-Assisted Intervention.Cham:Springer,2015:234-241.
[11] MILLETARI F,NAVAB N,AHMADI S A.V-net:fully convolutional neural networks for volumetric medical image segmentation[C]//Proceedings of the 4th International Conference on 3D Vision.Washington:IEEE Computer Society,2016:565-571.
[12] DAIMARY D,BORA M B,AMITAB K,et al.Brain tumor segmentation from MRI images using hybrid convolutional neural networks[J].Procedia Computer Science,2020,167:2419-2428.
[13] ZHANG J,JIANG Z,DONG J,et al.Attention gate resU-Net for automatic MRI brain tumor segmentation[J].IEEE Access,2020,8:58533-58545.
[14] CHEN H,DOU Q,YU L Q,et al.VoxResNet:Deep voxelwise residual networks for brain segmentation from 3D MR images[J].NeuroImage,2018,170:446-455.
[15] ZHOU Z,SIDDIQUEE M M R,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.
[16] HE K M,ZHANG X Y,REN S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[17] HE K M,ZHANG X Y,REN S Q,et al.Deep residual learningfor image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington:IEEE Computer Society,2016:770-778.
[18] HE K M,ZHANG X Y,REN S Q,et al.Identity mappings in deep residual networks[C]//European Conference on Computer Vision.Cham:Springer,2016:630-645.
[19] https//wiki.cancerimagingarchive.net/display/Public/TCGA-LGG.
[20] MIKOLAJCZYK A,GROCHOWSKI M.Data augmentation for improving deep learning in image classification problem[C]//International Interdisciplinary Phd Workshop.2018:117-122.
[21] SUDRE C H,LI W,VERCAUTEREN T,et al.Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations[C]//Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support.Cham:Springer,2017:240-248.
[22] ZHANG W L,LI R J,DENG H T,et al.Deep convolutionalneural networks for multi-modality isointense infant brain image segmentation[J].NeuroImage,2015,108:214-224.
[23] PEREIRA S,PINTO A,ALVES V,et al.Brain tumor segmentation using convolutional neural networks in MRI images[J].IEEE Transactions on Medical Imaging,2016,35(5):1240-1251.
[24] HAVAEI M,DAVY A,WARDE-FARLEY D,et al.Braintumor segmentation with deep neural networks[J].Medical Image Analysis,2017,35:18-31.
[25] ZHAO X,WU Y,SONG G,et al.A deep learning model integra-ting FCNNs and CRFs for brain tumor segmentation[J].Medical Image Analysis,2018,43:98-111.
[26] SERT E,OZYURT F,DOGANTEKIN A.A new approach for brain tumor diagnosis system:Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network[J].Medical Hypotheses,2019,133:109413.
[27] RAJA P M S,RANI A V.Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach[J].Biocybernetics and Biomedical Engineering,2020,40(1):440-453.
[28] AI L M,LI T D,LIAO F Y,et al.Magnetic resonance braintumor image segmentation based on attention U-Net[J].Laster &Optoelectronics Process,2020,57(14):279-286.
[29] RAI H M,CHATTERJEE K,DASHKEVICH S.Automaticand accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model[J].Biomedical Signal Processing and Control,2021,66:102477.
[30] HU C Y,SI M M,CHEN W.Brain MRI tumor segmentation method based on superpixel and mean[J].Journal of Chinese Computer Systems,2022,43(1):91-97.
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