基于改進(jìn)U型神經(jīng)網(wǎng)絡(luò)的腦出血CT圖像分割

胡敏; 周秀東; 黃宏程; 張光華; 陶洋

doi:10.11999/JEIT200996

基于改進(jìn)U型神經(jīng)網(wǎng)絡(luò)的腦出血CT圖像分割

doi: 10.11999/JEIT200996

胡敏¹,
周秀東¹,
黃宏程^{1, 2, ,},
張光華³,
陶洋^{1, 2}

1.
重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065
2.
重慶市通信軟件工程技術(shù)研究中心重慶 400065
3.
太原學(xué)院計(jì)算機(jī)科學(xué)與工程系太原 030000

基金項(xiàng)目: 國(guó)家重點(diǎn)研發(fā)計(jì)劃(2019YFB2102001)，山西省回國(guó)留學(xué)人員科研項(xiàng)目(2020-149)

詳細(xì)信息

作者簡(jiǎn)介:
胡敏：女，1971年生，教授，研究方向?yàn)閿?shù)字媒體技術(shù)、人機(jī)交互理論與技術(shù)應(yīng)用

周秀東：男，1995年生，碩士生，研究方向?yàn)橹悄芏嗝襟w信息處理

黃宏程：男，1979年生，副教授，研究方向?yàn)槿藱C(jī)融合計(jì)算智能、智能多媒體信息處理

張光華：男，1986年生，副教授，主要研究方向?yàn)榱孔狱c(diǎn)微型多光譜成像技術(shù)、多光譜圖像處理、醫(yī)學(xué)圖像處理

陶洋：男，1964年生，教授，研究方向?yàn)槿斯ぶ悄?、大?shù)據(jù)與計(jì)算智能

通訊作者:
黃宏程 ? ?huanghc@cqupt.edu.cn

中圖分類號(hào): TN911.73; TP391.41
計(jì)量
- 文章訪問(wèn)數(shù): 1563
- HTML全文瀏覽量: 1081
- PDF下載量: 231
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2020-11-25
- 修回日期: 2021-05-27
- 網(wǎng)絡(luò)出版日期: 2021-08-16
- 刊出日期: 2022-01-10

Computed-Tomography Image Segmentation of Cerebral Hemorrhage Based on Improved U-shaped Neural Network

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Engineering Research Center of Communication Software, Chongqing 400065, China
3.
Department of Computer Science and Engineering, Taiyuan University, Taiyuan 030000, China

Funds: The National Key Research and Development Program of China(2019YFB2102001), The Research Project of Shanxi Scholarship Council of China (2020-149)

摘要

摘要: 針對(duì)腦出血CT圖像病灶部位的多尺度性導(dǎo)致分割精度較低的問(wèn)題，該文提出一種基于改進(jìn)U型神經(jīng)網(wǎng)絡(luò)的圖像分割模型(AU-Net+)。首先，該模型利用U-Net中的編碼器對(duì)腦出血CT圖像特征編碼，將提出的殘差八度卷積(ROC)塊應(yīng)用到U型神經(jīng)網(wǎng)絡(luò)的跳躍連接部分，使不同層次的特征更好地融合；其次，對(duì)融合后的特征，分別引入混合注意力機(jī)制，用以提高對(duì)目標(biāo)區(qū)域的特征提取能力；最后，通過(guò)改進(jìn)Dice損失函數(shù)進(jìn)一步加強(qiáng)模型對(duì)腦出血CT圖像中小目標(biāo)區(qū)域的特征學(xué)習(xí)力度。為驗(yàn)證模型的有效性，在腦出血CT圖像數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn)，同U-Net, Attention U-Net, UNet++以及CE-Net相比，mIoU指標(biāo)分別提升了20.9%, 3.6%, 7.0%, 3.1%，表明AU-Net+模型具有更好的分割效果。
- 腦出血CT圖像分割 /
- 注意力機(jī)制 /
- Dice損失函數(shù) /
- 殘差八度卷積模塊
Abstract: In view of the problem of low segmentation accuracy caused by the multi-scale of the lesion location in Computed-Tomography (CT) images of cerebral hemorrhage, an image segmentation model based on Attention improved U-shaped neural Network plus (AU-Net+) is proposed. Firstly, the model uses the encoder in U-Net to encode the features of the CT image of cerebral hemorrhage, and applies the proposed Residual Octave Convolution (ROC) block to the jump connection part of the U-shaped neural network to make the features of different levels more blend well. Secondly, for the merged features, a hybrid attention mechanism is introduced to improve the feature extraction ability of the target area. Finally, the Dice loss function is improved to enhance further the feature learning of the model for small and medium-sized target regions in CT images of cerebral hemorrhage. To verify the performance of the model, the mIoU index is improved by 20.9%, 3.6%, 7.0%, 3.1% compared with U-Net, Attention U-Net, UNet++ and CE-Net respectively, which indicates that AU-Net+ model has better segmentation effect.
- Segmentation of Computed-Tomography (CT) images of cerebral hemorrhage /
- Attention mechanism /
- Dice loss function /
- Residual Octave Convolution block (ROC) module

HTML全文

圖 1 AU-Net+網(wǎng)絡(luò)框架

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圖 2 混合注意力機(jī)制

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圖 3 位置注意力機(jī)制

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圖 4 通道注意力機(jī)制

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圖 5 八度卷積計(jì)算過(guò)程

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圖 6 殘差八度卷積模塊(ROC)

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圖 7 實(shí)驗(yàn)流程圖

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圖 8 實(shí)驗(yàn)預(yù)處理效果對(duì)比圖

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圖 9 典型病例的分割結(jié)果

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圖 10 AU-Net+模型訓(xùn)練曲線

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圖 11 分割效果圖

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圖 12 實(shí)驗(yàn)結(jié)果分割效果圖

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圖 13 ${y_{{\rm{pred}}}}$的指數(shù)對(duì)分割的影響

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表 1 AU-Net+網(wǎng)絡(luò)結(jié)構(gòu)

編碼器-解碼器	跳躍連接
conv2d_1 (UConv2D)	up_sampling2d_4 (Conv2DTrans)
max_pooling2d_1 (MaxPooling2D)	concatenate_4 (Concatenate)
conv2d_2 (UConv2D)	roc_1(Roc)
max_pooling2d_2 (MaxPooling2D)	up_sampling2d_5 (Conv2DTrans)
conv2d_3 (UConv2D)	concatenate_5 (Concatenate)
max_pooling2d_3 (MaxPooling2D)	roc_2(Roc)
conv2d_4 (UConv2D)	up_sampling2d_6 (Conv2DTrans)
dropout_1 (Dropout)	add_1 (Add)
up_sampling2d_1 (Conv2DTrans)	att_1(Attention)
concatenate_1 (Concatenate)	up_sampling2d_7 (Conv2DTrans)
conv2d_5 (UConv2D)	concatenate_6 (Concatenate)
up_sampling2d_2 (Conv2DTrans)	roc_3(Roc)
concatenate_2 (Concatenate)	up_sampling2d_8 (Conv2DTrans)
conv2d_6 (UConv2D)	add_2 (Add)
up_sampling2d_3 (Conv2DTrans)	att_2(Attention)
concatenate_3 (Concatenate)	up_sampling2d_9 (Conv2DTrans)
conv2d_7 (UConv2D)	add_3 (Add)
conv2d_8 (EConv2D)	att_3(Attention)

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表 2 分類結(jié)果的混淆矩陣

預(yù)測(cè)值\實(shí)際值	正樣本	負(fù)樣本
正樣本	${\rm{TP}}$	${\rm{FP}}$
負(fù)樣本	${\rm{FN}}$	${\rm{TN}}$

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表 3 評(píng)價(jià)指標(biāo)的統(tǒng)計(jì)結(jié)果

評(píng)價(jià)指標(biāo)	mIoU	VOE	Recall	DICE	Specificity
均值	0.862	0.021	0.912	0.924	0.987
方差	0.009	0.001	0.004	0.002	0.002
中值	0.901	0.023	0.935	0.953	0.998

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表 4 實(shí)驗(yàn)結(jié)果對(duì)比

方法(參數(shù)量)	迭代次數(shù)	mIoU	VOE	Recall	DICE	Specificity
U-Net (31377858)	4600	0.653	0.043	0.731	0.706	0.974
Attention U-Net(31901542)	4600	0.826	0.021	0.861	0.905	0.977
U-Net++(36165192)	4800	0.792	0.025	0.833	0.883	0.976
CE-Net (29003094)	4500	0.831	0.022	0.873	0.911	0.981
AU-Net+(37646416)	5000	0.862	0.021	0.912	0.924	0.987

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表 5 混合注意力機(jī)制和ROC結(jié)構(gòu)分析指標(biāo)對(duì)比

模型	mIoU	VOE	Recall	DICE	Specificity
Network_1	0.661	0.042	0.735	0.714	0.976
Network_2	0.835	0.025	0.841	0.893	0.974
Network_3	0.781	0.041	0.744	0.723	0.985
Network_4	0.842	0.023	0.862	0.905	0.986
AU-Net+	0.862	0.021	0.912	0.924	0.987

下載: 導(dǎo)出CSV

表 6 實(shí)驗(yàn)結(jié)果對(duì)比

模型	參數(shù)量	mIoU	VOE	Recall	DICE	Specificity
Attention U-Net^*	38654416	0.804	0.027	0.853	0.896	0.956
Attention U-Net	31901542	0.826	0.021	0.861	0.905	0.977
AU-Net+	37646416	0.862	0.021	0.912	0.924	0.987

下載: 導(dǎo)出CSV

參考文獻(xiàn)(19)

[1]	談山峰, 方芳, 陳兵, 等. 腦疝后腦梗塞預(yù)后因素分析[J]. 海南醫(yī)學(xué), 2014, 25(3): 400–402. doi: 10.3969/j.issn.1003-6350.2014.03.0152 TAN Shanfeng, FANG Fang, CHEN Bing, et al. Analysis of prognostic factors of cerebral infarction after cerebral hernia[J]. Hainan Medical Journal, 2014, 25(3): 400–402. doi: 10.3969/j.issn.1003-6350.2014.03.0152
[2]	SUN Mingjie, HU R, YU Huimin, et al. Intracranial hemorrhage detection by 3D voxel segmentation on brain CT images[C]. 2015 International Conference on Wireless Communications & Signal Processing (WCSP), Nanjing, China, 2015: 1–5. doi: 10.1109/WCSP.2015.7341238.
[3]	WANG Nian, TONG Fei, TU Yongcheng, et al. Extraction of cerebral hemorrhage and calculation of its volume on CT image using automatic segmentation algorithm[J]. Journal of Physics: Conference Series, 2019, 1187(4): 042088. doi: 10.1088/1742-6596/1187/4/042088
[4]	BHADAURIA H S, SINGH A, and DEWAL M L. An integrated method for hemorrhage segmentation from brain CT Imaging[J]. Computers & Electrical Engineering, 2013, 39(5): 1527–1536. doi: 10.1016/j.compeleceng.2013.04.010
[5]	SHAHANGIAN B and POURGHASSEM H. Automatic brain hemorrhage segmentation and classification in CT scan images[C]. 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), Zanjan, Iran, 2013: 467–471. doi: 10.1109/IranianMVIP.2013.6780031.
[6]	KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90. doi: 10.1145/3065386
[7]	WANG Shuxin, CAO Shilei, WEI Dong, et al. LT-Net: Label transfer by learning reversible voxel-wise correspondence for one-shot medical image segmentation[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 9159–9168. doi: 10.1109/CVPR42600.2020.00918.
[8]	RONNEBERGER O, FISCHER P, and BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]. The 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241. doi: 10.1007/978-3-319-24574-4_28.
[9]	彭佳林, 揭萍. 基于序列間先驗(yàn)約束和多視角信息融合的肝臟CT圖像分割[J]. 電子與信息學(xué)報(bào), 2018, 40(4): 971–978. doi: 10.11999/JEIT170933 PENG Jialin and JIE Ping. Liver segmentation from CT image based on sequential constraint and multi-view information fusion[J]. Journal of Electronics &Information Technology, 2018, 40(4): 971–978. doi: 10.11999/JEIT170933
[10]	MILLETARI F, NAVAB N, and AHMADI S A. V-Net: Fully convolutional neural networks for volumetric medical image segmentation[C]. 2016 Fourth International Conference on 3D Vision (3DV), Stanford, USA, 2016: 565–571. doi: 10.1109/3DV.2016.79.
[11]	GUAN S, KHAN A A, SIKDAR S, et al. Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 24(2): 568–576. doi: 10.1109/JBHI.2019.2912935
[12]	XIAO Xiao, LIAN Shen, LUO Zhiming, et al. Weighted Res-UNet for high-quality retina vessel segmentation[C]. 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China, 2018: 327–331. doi: 10.1109/ITME.2018.00080.
[13]	OKTAY O, SCHLEMPER J, LE FOLGOC L, et al. Attention U-Net: Learning where to look for the pancreas[C]. The 1st Conference on Medical Imaging with Deep Learning, Amsterdam, Netherlands, 2018: 1–10.
[14]	IBTEHAZ N and RAHMAN M S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 2020, 121: 74–87. doi: 10.1016/j.neunet.2019.08.025
[15]	ALOM M Z, YAKOPCIC C, TAHA T M, et al. Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net)[C]. NAECON 2018-IEEE National Aerospace and Electronics Conference, Dayton, USA, 2018: 228–233. doi: 10.1109/NAECON.2018.8556686.
[16]	ZHOU Zongwei, RAHMAN M M, TAJBAKHSH N, et al. UNet++: Redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE Transactions on Medical Imaging, 2020, 39(6): 1856–1867. doi: 10.1109/TMI.2019.2959609
[17]	GU Zaiwang, CHENG Jun, FU Huazhu, et al. CE-Net: Context encoder network for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281–2292. doi: 10.1109/TMI.2019.2903562
[18]	FU Jun, LIU Jing, TIAN Haijie, et al. Dual attention network for scene segmentation[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 3141–3149. doi: 10.1109/CVPR.2019.00326.
[19]	CHEN Yunpeng, FAN Haoqi, XU Bing, et al. Drop an Octave: Reducing spatial redundancy in convolutional neural networks with Octave convolution[C]. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, South Korea, 2019: 3434–3443. doi: 10.1109/ICCV.2019.00353.