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

doi: 10.11999/JEIT200996 cstr: 32379.14.JEIT200996

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

1.
重慶郵電大學通信與信息工程學院重慶 400065
2.
重慶市通信軟件工程技術研究中心重慶 400065
3.
太原學院計算機科學與工程系太原 030000

基金項目: 國家重點研發(fā)計劃(2019YFB2102001)，山西省回國留學人員科研項目(2020-149)

詳細信息

作者簡介:
胡敏：女，1971年生，教授，研究方向為數(shù)字媒體技術、人機交互理論與技術應用

周秀東：男，1995年生，碩士生，研究方向為智能多媒體信息處理

黃宏程：男，1979年生，副教授，研究方向為人機融合計算智能、智能多媒體信息處理

張光華：男，1986年生，副教授，主要研究方向為量子點微型多光譜成像技術、多光譜圖像處理、醫(yī)學圖像處理

陶洋：男，1964年生，教授，研究方向為人工智能、大數(shù)據(jù)與計算智能

通訊作者:
黃宏程 ? ?huanghc@cqupt.edu.cn

中圖分類號: TN911.73; TP391.41
計量
- 文章訪問數(shù): 1651
- HTML全文瀏覽量: 1189
- PDF下載量: 234
- 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: 針對腦出血CT圖像病灶部位的多尺度性導致分割精度較低的問題，該文提出一種基于改進U型神經(jīng)網(wǎng)絡的圖像分割模型(AU-Net+)。首先，該模型利用U-Net中的編碼器對腦出血CT圖像特征編碼，將提出的殘差八度卷積(ROC)塊應用到U型神經(jīng)網(wǎng)絡的跳躍連接部分，使不同層次的特征更好地融合；其次，對融合后的特征，分別引入混合注意力機制，用以提高對目標區(qū)域的特征提取能力；最后，通過改進Dice損失函數(shù)進一步加強模型對腦出血CT圖像中小目標區(qū)域的特征學習力度。為驗證模型的有效性，在腦出血CT圖像數(shù)據(jù)集上進行實驗，同U-Net, Attention U-Net, UNet++以及CE-Net相比，mIoU指標分別提升了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)絡框架

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圖 2 混合注意力機制

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圖 3 位置注意力機制

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圖 4 通道注意力機制

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圖 5 八度卷積計算過程

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圖 6 殘差八度卷積模塊(ROC)

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圖 7 實驗流程圖

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圖 8 實驗預處理效果對比圖

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圖 9 典型病例的分割結果

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圖 10 AU-Net+模型訓練曲線

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圖 11 分割效果圖

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圖 12 實驗結果分割效果圖

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

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表 1 AU-Net+網(wǎng)絡結構

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 分類結果的混淆矩陣

預測值\實際值	正樣本	負樣本
正樣本	${\rm{TP}}$	${\rm{FP}}$
負樣本	${\rm{FN}}$	${\rm{TN}}$

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表 3 評價指標的統(tǒng)計結果

評價指標	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 實驗結果對比

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 混合注意力機制和ROC結構分析指標對比

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

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表 6 實驗結果對比

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

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