面向医学图像分割的超像素U-Net网络设计
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摘要
近年来,超像素在医学图像处理领域的应用愈加广泛,现有的方法取得了较好的效果,如LAW, SLIC等.然而,这些方法在处理医学图像得到超像素时,位于组织边缘像素点的划分仍存在类别模糊问题.为此,提出一种基于U-Net网络的超像素分割方法.首先,通过双边滤波模型过滤外部噪声,增强超像素信息;然后,结合U-Net卷积网络学习图像特征.该方法为U-Net网络中每个特征尺度的卷积层后嵌入一个规范层,用于增强网络对参数的敏感性.实验结果表明,该方法有效提高了医学图像超像素的分割精度,与groundtruth相比,其改善了超像素边缘分类的准确性,优化了超像素分割结果,在精确度、召回率、F-measure和分割速度等性能指标上均取得了更好的效果.
In recent years, the superpixel methods have been widely used in the field of medical image processing and achieved good results, such as LAW, SLIC, etc. However, there were still some problems of fuzzy classification at the edge of the tissues when these methods were used to obtain superpixels. A superpixel optimization approach based on U-Net architecture was proposed in this paper. Firstly, a bilateral filtering(BF) operation was adopted to eliminate external noisy effects at the beginning of the network, and enhance the grayscale information.Then, via combining with U-Net networks, the whole model can learn the image features and output the optimized results for the superpixel map. In terms of network design, a normalization layer was embedded behind the convolution layer at each feature-scales, in order to strengthen the sensitivity of the parameters. Experimental results show that the classification accuracy in superpixel edge is significantly improved compared with the ground truth. Moreover, this method has achieved better results in precision, recall, F-measure and computational efficiency than other classic methods.
In recent years, the superpixel methods have been widely used in the field of medical image processing and achieved good results, such as LAW, SLIC, etc. However, there were still some problems of fuzzy classification at the edge of the tissues when these methods were used to obtain superpixels. A superpixel optimization approach based on U-Net architecture was proposed in this paper. Firstly, a bilateral filtering(BF) operation was adopted to eliminate external noisy effects at the beginning of the network, and enhance the grayscale information.Then, via combining with U-Net networks, the whole model can learn the image features and output the optimized results for the superpixel map. In terms of network design, a normalization layer was embedded behind the convolution layer at each feature-scales, in order to strengthen the sensitivity of the parameters. Experimental results show that the classification accuracy in superpixel edge is significantly improved compared with the ground truth. Moreover, this method has achieved better results in precision, recall, F-measure and computational efficiency than other classic methods.
引文
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[2]Moore A P,Prince S J D,Warrell J,et al.Superpixel lattices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2008:1-8
[3]Liu M Y,Tuzel O,Ramalingam S,et al.Entropy rate superpixel segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2011:2097-2104
[4]Levinshtein A,Stere A,Kutulakos K N,et al.TurboPixels:fast superpixels using geometric flows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(12):2290-2297
[5]Comaniciu D,Meer P.Mean shift:a robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619
[6]Achanta R,Shaji A,Smith K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282
[7]Liu Y J,Yu M J,Li B J,et al.Intrinsic manifold SLIC:a simple and efficient method for computing content-sensitive superpixels[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(3):653-666
[8]Shen J B,Hao X P,Liang Z Y,et al.Real-time superpixel seg-mentation by DBSCAN clustering algorithm[J].IEEETransactions on Image Processing,2016,25(12):5933-5942
[9]Peng J T,Shen J B,Yao A,et al.Superpixel optimization using higher order energy[J].IEEE Transactions on Circuits and Systems for Video Technology,2016,26(5):917-927
[10]Long J,Shelhamer E,Darrell T.Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2015:3431-3440
[11]Badrinarayanan V,Kendall A,Cipolla R.SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495
[12]Lin G S,Milan A,Shen C H,et al.Refinenet:multi-path refinement networks for high-resolution semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEEComputer Society Press,2017:5168-5177
[13]Zhao H S,Shi J P,Qi X J,et al.Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2017:6230-6239
[14]Ronneberger O,Fischer P,Brox T.U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of the Medical Image Computing and Computer-Assisted Intervention.Heidelberg:Springer Press,2015:234-241
[15]Paris S,Kornprobst P,Tumblin J,et al.Bilateral filtering:theory and applications[J].Foundations and Trends?in Computer Graphics and Vision,2009,4(1):1-73
[16]Ioffe S,Szegedy C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning.Lille:PMLR Press,2015:448-456
[17]Salimans T,Kingma D P.Weight normalization:a simple reparameterization to accelerate training of deep neural networks[C]//Proceedings of the Advances in Neural Information Processing Systems.Cambridge:MIT Press,2016:901-909
[18]Nan Dong,Bi Duyan,Ma Shiping,et al.An adaptive bilateral filtering method based on parameter estimation[J].Journal of Central South University:Science and Technology,2014,45(11):3840-3845(in Chinese)(南栋,毕笃彦,马时平,等.一种基于参数估计的自适应双边滤波算法[J].中南大学学报:自然科学版,2014,45(11):3840-3845)
[19]Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the Advances in Neural Information Processing Systems.Cambridge:MIT Press,2012:1097-1105
[20]Shen J B,Du Y F,Wang W G,et al.Lazy random walks for superpixel segmentation[J].IEEE Transactions on Image Processing,2014,23(4):1451-1462
[21]Powers D M W.Evaluation:from precision,recall and F-measure to ROC,informedness,markedness and correlation[J].Journal of Machine Learning Technologies,2011,2(1):37-63
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