融合候选区域提取与SSAE深度特征学习的心脏MR图像左心室检测
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摘要
左心室检测在计算机辅助心脏MR图像诊断方面具有重要价值,针对由于成像质量、部分容积效应、目标复杂多变等因素影响,导致左心室自动检测准确度较低的问题,提出一种融合候选区域提取与栈式稀疏自编码器(SSAE)深度特征学习的心脏MR图像左心室检测方法.在候选区域提取阶段,先用超像素算法产生初始区域,然后对SSAE学习到的深度特征采用层次聚类算法生成候选区域;在检测阶段,先使用SSAE提取候选区域的深度特征,然后训练SVM分类器对候选区域进行分类,并使用难分负样本挖掘算法对模型进行调节.对心脏图谱数据集左心室目标检测的实验结果表明,相对于手工特征及基于候选区域等方法,该方法取得了有竞争力的检测精度.
Automatic detection of left ventricle(LV) is an important step for further analyzing cardiac MR images. However, due to the image acquisition, partial volume effect, low resolution and high similarity to the surroundings, it is a challenging task for improving LV detection accuracy. In this paper an automatic detection method is proposed by combining region proposals and deep Stacked Sparse Auto-encoder(SSAE) learnt features. It consists of two components: 1) At the stage of proposing candidate regions, a superpixel algorithm is firstly adopted to generate initial regions, then a hierarchical clustering algorithm using deep SSAE learnt feature is employed to make the final candidates; 2) At the stage of detection, a SSAE network is used to extract deep feature of the resulting candidates, and the learnt feature is used to train a linear C-SVM classifier. Furthermore, a hard negative mining strategy is added for tuning the model adaptive to the sample imbalance problem. Experimental results of left ventricle detection on the Cardiac Atlas Project(CAP) data set show that, compared to the representative hand-crafted or region proposal based methods, the proposed method achieves competitive results.
Automatic detection of left ventricle(LV) is an important step for further analyzing cardiac MR images. However, due to the image acquisition, partial volume effect, low resolution and high similarity to the surroundings, it is a challenging task for improving LV detection accuracy. In this paper an automatic detection method is proposed by combining region proposals and deep Stacked Sparse Auto-encoder(SSAE) learnt features. It consists of two components: 1) At the stage of proposing candidate regions, a superpixel algorithm is firstly adopted to generate initial regions, then a hierarchical clustering algorithm using deep SSAE learnt feature is employed to make the final candidates; 2) At the stage of detection, a SSAE network is used to extract deep feature of the resulting candidates, and the learnt feature is used to train a linear C-SVM classifier. Furthermore, a hard negative mining strategy is added for tuning the model adaptive to the sample imbalance problem. Experimental results of left ventricle detection on the Cardiac Atlas Project(CAP) data set show that, compared to the representative hand-crafted or region proposal based methods, the proposed method achieves competitive results.
引文
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[29]Fonseca C G,Backhaus M,Bluemke D A,et al.The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart[J].Bioinformatics,2011,27(16):2288-2295
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[2]Zhou X R,Wang S,Chen H Y,et al.Automatic localization of solid organs on 3D CT images by a collaborative majority voting decision based on ensemble learning[J].Computerized Medical Imaging and Graphics,2012,36(4):304-313
[3]Criminisi A,Shotton J,Bucciarelli S.Decision forests with long-range spatial context for organ localization in CT volumes[C]//Proceedings of the MICCAI Workshop on Probabilistic Models for Medical Image Analysis.Heidelberg:Springer,2009:69-80
[4]Zheng Y F,Comaniciu D.Part-based object detection and segmentation[M].Heidelberg:Springer,2014:103-135
[5]Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2014:580-587
[6]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
[7]Girshick R.Fast R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2015:1440-1448
[8]Ren S Q,He K M,Girshick R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149
[9]Dai J F,Li Y,He K M,et al.R-FCN:Object detection via region-based fully convolutional networks[C]//Proceedings of International Conference on Neural Information Processing Systems.Cambridge:MIT Press,2016:379-387
[10]Yan Z N,Zhan Y Q,Peng Z G,et al.Bodypart recognition using multi-stage deep learning[M]//Lecture Notes in Computer Science.Heidelberg:Springer,2015,9123:449-461
[11]Roth H R,Lee C T,Shin H C,et al.Anatomy-specific classification of medical images using deep convolutional nets[C]//Proceedings of the 12th IEEE International Symposium on Biomedical Imaging.Los Alamitos:IEEE Computer Society Press,2015:101-104
[12]de Vos B D,Wolterink J M,de Jong P A,et al.2D image classification for 3D anatomy localization:employing deep convolutional neural networks[C]//Proceedings of SPIE.Bellingham:Socitey of Photo-Optical Instrumentation Engineers Press,2016,9784:97841Y
[13]Redmon J,Divvala S,Girshick R,et al.You only look once:unified,real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2016:779-788
[14]Liu W,Anguelov D,Erhan D,et al.SSD:single shot multibox detector[M]//Lecture Notes in Computer Science.Heidelberg:Springer,2016,9905:21-37
[15]Vaillant R,Monrocq C,Le Cun Y.Original approach for the localisation of objects in images[J].IEE Proceedings-Vision,Image and Signal Processing,1994,141(4):245-250
[16]Nowlan S J,Platt J C.A convolutional neural network hand tracker[C]//Proceedings of Advances in Neural Information Processing Systems.Cambridge:MIT Press,1995:901-908
[17]Viola P,Jones M J.Robust real-time face detection[J].International Journal of Computer Vision,2004,57(2):137-154
[18]Zhao Guangjun,Wang Xuchu,Niu Yanmin,et al.Deep SAE feature learning based segmentation for digital human brain image[J].Journal of Computer-Aided Design&Computer Graphics,2016,28(8):1297-1305(in Chinese)(赵广军,王旭初,牛彦敏,等.基于SAE深度特征学习的数字人脑切片图像分割[J].计算机辅助设计与图形学学报,2016,28(8):1297-1305)
[19]Uijlings J R R,van de Sande K E A,Gevers T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171
[20]Zitnick C L,Dollár P.Edge boxes:locating object proposals from edges[M]//Lecture Notes in Computer Science.Heidelberg:Spring,2014,8693:391-405
[21]Fulkerson B,Vedaldi A,Soatto S.Class segmentation and object localization with superpixel neighborhoods[C]//Proceedings of the 12th IEEE International Conference on Computer Vision.Los Alamitos:IEEE Computer Society Press,2009:670-677
[22]Achanta R,Shaji A,Smith K,et al.SLIC superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2274-2282
[23]Chen Chang’an,Wu Xiaofeng,Wang Bin,et al.Video saliency detection using dynamic fusion of spatial-temporal features in complex background with disturbance[J].Journal of ComputerAided Design&Computer Graphics,2016,28(5):802-812(in Chinese)(陈昶安,吴晓峰,王斌,等.复杂扰动背景下时空特征动态融合的视频显著性检测[J].计算机辅助设计与图形学学报,2016,28(5):802-812)
[24]Neubeck A,van Gool L.Efficient non-maximum suppression[C]//Proceedings of the 18th International Conference on Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2006,3:850-855
[25]Wan L,Eigen D,Fergus R.End-to-end integration of a convolution network,deformable parts model and non-maximum suppression[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2015:851-859
[26]Girshick R B,Felzenszwalb P F,Mcallester D.Object detection with grammar models[C]//Proceedings of the 24th International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2011:442-450
[27]Bell S,Zitnick C L,Bala K,et al.Inside-outside net:detecting objects in context with skip pooling and recurrent neural networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2016:2874-2883
[28]Felzenszwalb P F,Girshick R B,Mc Allester D,et al.Object detection with discriminatively trained part-based models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(9):1627-1645
[29]Fonseca C G,Backhaus M,Bluemke D A,et al.The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart[J].Bioinformatics,2011,27(16):2288-2295
[30]Coates A,Ng A,Lee H.An analysis of single-layer networks in unsupervised feature learning[J].Proceedings of Machine Learning Research,2011,15:215-223
[31]Le Q V,Ngiam J,Chen Z H,et al.Tiled convolutional neural networks[C]//Proceedings of the 23rd International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2010,1:1279-1287