北京生物醫(yī)學(xué)工程

基于改進(jìn)Attention U-Net的膽囊自動(dòng)分割模型研究

Research on gallbladder automatic segmentation model based on improved Attention U-Net

作者：尹梓名孫大運(yùn) 任泰周雷李永盛王廣義王傳磊曹宏劉穎斌束翌俊

單位：上海理工大學(xué)醫(yī)療器械與食品學(xué)院(上海200093) ,上海交通大學(xué)醫(yī)學(xué)院附屬新華醫(yī)院普外科(上海200092),上海交通大學(xué)醫(yī)學(xué)院附屬仁濟(jì)醫(yī)院膽胰外科(上海200127),上海市膽道疾病研究重點(diǎn)實(shí)驗(yàn)室(上海200092) ,癌基因及相關(guān)基因國家重點(diǎn)實(shí)驗(yàn)室(上海200127),吉林大學(xué)白求恩第一醫(yī)院肝膽胰外一科(長春130021) ,吉林大學(xué)中日聯(lián)誼醫(yī)院普外科(長春130033)  通訊作者：劉穎斌，E-mail: [email protected]，束翌俊，E-mail: [email protected]

關(guān)鍵詞：深度學(xué)習(xí); 膽囊; 圖像分割; U-NET; 注意力機(jī)制

分類號(hào)：R318.04;TP391.5

出版年·卷·期（頁碼）：2021·40·4（346-353）

摘要：

目的基于多尺度注意力融合機(jī)制，提出改進(jìn)Attention U-Net的膽囊自動(dòng)分割模型，提高膽囊自動(dòng)分割模型的性能，以輔助醫(yī)生進(jìn)行臨床診斷。方法首先選取2017年1月-2019年12月上海交通大學(xué)醫(yī)學(xué)院附屬新華醫(yī)院普外科、吉林大學(xué)第一醫(yī)院肝膽胰外一科和吉林大學(xué)中日聯(lián)誼醫(yī)院普外科收治的88例病理診斷明確的膽囊癌，28例慢性膽囊炎膽囊結(jié)石患者和29例正常膽囊患者，構(gòu)建膽囊分割數(shù)據(jù)集，然后通過對醫(yī)學(xué)常用深度學(xué)習(xí)圖像分割方法U-Net和Attention U-Net進(jìn)行分析，提出基于多尺度融合注意力機(jī)制改進(jìn)的Attention U-Net方法，并設(shè)計(jì)實(shí)驗(yàn)對三種方法進(jìn)行對比評估。結(jié)果提出的改進(jìn)Attention U-Net方法在驗(yàn)證集上的交并比閾值（IoU）分?jǐn)?shù)，Dice系數(shù)，檢測精度（Precision）和召回率（Recall）分別為0.72，0.84，0.92，0.79，全部優(yōu)于傳統(tǒng)U-Net和Attention U-Net方法。結(jié)論本文提出了基于多尺度融合注意力機(jī)制改進(jìn)的Attention U-Net模型，其性能優(yōu)于U-Net和Attention U-Net，證明了本方法中改進(jìn)的注意力機(jī)制可以很好地改善U-Net模型在膽囊影像上的分割結(jié)果。

Objective According to multi-scale attention fusion mechanism, an improved gallbladder automatic segmentation model based on attention u-net is proposed to assist doctors in clinical diagnosis. Methods 88 cases of gallbladder cancer, 28 cases of chronic cholecystitis with gallbladder stones and 29 cases of normal gallbladder were selected from general surgery department of Xinhua Hospital Affiliated to Medical College of Shanghai Jiaotong University, first department of hepatobiliary and pancreatic surgery of first hospital of Jilin University, general surgery department of China-Japan Friendship Hospital, Jilin University from January 2017 to December 2019. Through the analysis of the commonly used deep learning image segmentation methods U-Net and Attention U-Net, we proposed an improved Attention U-Net method based on multi-scale fusion attention mechanism, and designed experiments to verify and evaluate the three methods. Results the results showed that the IOU score, Dice coefficient, precision and recall rate of the improved Attention U-Net method were 0.72, 0.84, 0.92 and 0.79 respectively, which were all better than those of the traditional U-Net and Attention U-Net methods. Conclusions the performance of our improved Attention U-Net model is better than that of U-Net and Attention U-Net, which proves that the improved attention mechanism in this method can improve the segmentation result of U-Net model in gallbladder image.

參考文獻(xiàn)：

[1]孫曉敏, 徐萍, 馬志紅,等. 上海松江地區(qū)膽囊良性疾病的流行病學(xué)調(diào)查30901例[J]. 世界華人消化雜志, 2011, 19(27):2881-2885.

??? Sun XM, Xu P, Ma ZH, et al. An epidemiological survey of benign gallbladder disease in Songjiang District of Shanghai, China[J]. World Chinese Journal of Digestology, 2011, 019(027):2881-2885.

[2] 朱忠偉,李福軍,胡柯銘,等.寧波鎮(zhèn)海地區(qū)膽囊疾病的流行病學(xué)調(diào)查[J].現(xiàn)代實(shí)用醫(yī)學(xué),2012,24(11):1241-1242.

[3] 趙登秋, 張建淮, 王衛(wèi)東. 江蘇淮安地區(qū)膽囊疾病的流行病學(xué)調(diào)查[J]. 消化外科, 2004, 3(1):39-40.

??? Zhao DQ, Zhang JH, Wang WD, et al. An epidemic survey of gallbladder disease in Huaian district of Jiangshu Province[J]. Journal of Digestive Surgery, 2004, 3(1):39-40.

[4] ?Li ML, Zhang Z, Li XG, et al. Whole-exome and targeted gene sequencing of gallbladder carcinoma identifies recurrent mutations in the ErbB pathway[J]. Nature Genetics, 2014, 46(8):872-876.

[5] Naser MA, Deen MJ. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images[J]. Computers in Biology and Medicine, 2020, 121:103758.

[6] Hu HF, Pan N, Wang JY, et al. Automatic segmentation of left ventricle from cardiac MRI via deep learning and region constrained dynamic programming[J]. Neurocomputing, 2019, 347: 139-148.

[7] Almotairi S, Kareem G, Aouf M, et al. Liver tumor segmentation in CT scans using modified SegNet[J]. Sensors, 2020, 20(5):1516.

[8] 譚丹滟. 腹部CT影像中膽囊分割系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D].武漢:華中科技大學(xué), 2017.

??? Tan DY. Design and implementation of gallbladder segmentation system in abdominal CT images[D]. Wuhan: Huazhong University of Science and Technology, 2017.

[9] 段雪琦. 腹部CT影像中的膽管分割方法研究[D].武漢:華中科技大學(xué), 2017.

??? Duan XQ. A research on bile duct segmentation in abdominal CT images[D]. Wuhan: Huazhong University of Science and Technology, 2017.

[10] Huang YF, Xu ZZ, Yan CR. Automatic gallbladder location and segmentation based on anatomical knowledge[J]. Applied Mechanics & Materials, 2014, 556-562:4697-4700.

[11] Lian J, Ma Y, Ma Y, et al. Automatic gallbladder and gallstone regions segmentation in ultrasound image[J]. International Journal of Computer Assisted Radiology and Surgery, 2017, 12(4):553-568.

[12] Li C, Tan Y, Chen W, et al. ANU-Net: attention-based nested U-Net to exploit full resolution features for medical image segmentation[J]. Computers & Graphics, 2020, 90: 11-20.

[13] Alom MZ, Yakopcic C, Hasan M, et al. Recurrent residual U-Net for medical image segmentation[J]. Journal of Medical Imaging, 2019, 6(1): 014006.

[14] Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[M]// International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Switzerland: ?Springer, Cham, 2015: 234-241.

[15] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE Press, 2016: 770-778.

[16] 劉楊,朱斌, 何健,等. 意外膽囊癌的影像學(xué)特征及誤漏診分析[J]. 中國醫(yī)藥導(dǎo)報(bào)，2020，17(1)：161-164.

Liu Y, Zhu B, He J, et al. Analysis of imaging features of unsuspected gallbladder cancer and causes of misdiagnosis or missed diagnosis[J]. China Medical Herald，2020，17(1)：161-164.

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