北京生物醫(yī)學(xué)工程

基于深度監(jiān)督全卷積神經(jīng)網(wǎng)絡(luò)的 MRI腦圖像語義分割算法

Semantic segmentation algorithm for MRI brain image based on deeply supervised fully convolutional network

作者：黃星奕丘子明許燕

單位：北京航空航天大學(xué)深圳研究院(北京 100091)

關(guān)鍵詞：語義分割; 深度學(xué)習(xí); 醫(yī)學(xué)圖像; 神經(jīng)網(wǎng)絡(luò); 機器學(xué)習(xí)

分類號：R318.04

出版年·卷·期（頁碼）：2019·38·3（277-282）

摘要：

目的依據(jù) 臨床診斷對 MRI 腦圖像自動分割算法的需求, 基于卷積神經(jīng) 網(wǎng) 絡(luò)(convolutional neural networks,CNN)設(shè)計了一種端到端的深度監(jiān)督全卷積網(wǎng)絡(luò)( deeply supervised fully convolutional network,DS-FCN)以解決腦圖像中腦組織的自動分割問題? 方法針對三維 MRI 腦圖像,先將體數(shù)據(jù)切割成二維圖像切片,在 FCN 網(wǎng)絡(luò)結(jié)構(gòu)的基礎(chǔ)上,加入了深度監(jiān)督機制,即在特征提取的多層級結(jié)構(gòu)中提前得到損失值反饋。結(jié)果以三維 MRI 腦圖像公開數(shù)據(jù)集 LPBA-40 為實驗數(shù)據(jù),56 類腦組織的準(zhǔn)確率(precision rate)，召回率(recall rate)，F(xiàn)1 評估值分別為74.40%，74.82%，73.75%，測試速率為 152 ms。結(jié)論通過引入深度監(jiān)督結(jié)構(gòu),改進后的 DS-FCN 在 MRI 腦組織分割任務(wù)中得到了更精準(zhǔn)的分割效果。

Objective In this paper, we present an end?to?end brain image semantic segmentation system based on the convolutional neural networks(CNN) framework to get segmentation of brain structures automatically,corresponding to the requirement of clinic automatic segmentation for MRI brain images ( DS-FCN,deeply supervised fully convolutional network). Methods For 3D MRI brain images,we firstly cut the volume data into 2D image slices.Then based on the FCN architecture,deep supervision is introduced into our system to get loss feedback in feature extraction across multiple scales. Results We select LONI,LPBA,40 as our experiment dataset which has 56 categories annotation of brain tissue. The precision,recall,F1,measure of our method reaches 74.40%,74.82% and 73.75%,which costs 152 ms ( on a typical GPU) to produce a segmentation map in testing phase. Conclusions Guided by the deeply multi?scale supervision, end?to?end segmentation system DS-FCN shows better results in experiment.

參考文獻：

[ 1 ] Khayati R, Vafadust M, Towhidkhah F, et al. Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model [ J]. Computers in Biology & Medicine, 2008,38 ( 3):

379-390.

[ 2 ] Jr CRJ,Bernstein MA,Fox NC,et al. The Alzheimer's Disease Neuroimaging Initiative ( ADNI): MRI Methods [ J]. Journal of Magnetic Resonance Imaging,2008,27(4):685-691.

[ 3 ] Xu Y,Jia Z,Ai Y,et al. Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation [ C ] / / IEEE. Brisbane, Australia: IEEE International Conference on Acoustics, Speech and Signal Processing,2015:947-951.

[ 4 ] Chen H,Wang X,Heng P A. Automated mitosis detection with deep regression networks [ C] / / IEEE, International Symposium on Biomedical Imaging. Prague: Czech Republic, 2016: 1204-1207.

[ 5 ] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18:1527-1554.

[ 6 ] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks [ C ] / / Advances in neural information processing systems. Lake Tahoe,Nevada:MIT Press,2012: 1097-1105.

[ 7 ] Szegedy C,Liu W,Jia Y,et al. Going deeper with convolutions [C] / / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA:IEEE,2015: 1-9.

[ 8 ] He K,Zhang X,Ren S,et al. Deep residual learning for image recognition [ C ] / / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV:IEEE,2016: 770-778.

[ 9 ] Long J,Shelhamer E,Darrell T. Fully convolutional networks for semantic segmentation [ C ] / / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston, MA:IEEE,2015: 3431-3440.

[10] Zheng S, Jayasumana S, Romera?Paredes B, et al. Conditional random fields as recurrent neural networks [ C] / / Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile:IEEE, 2015: 1529-1537.

[11] Lee CY, Xie S, Gallagher PW, et al. Deeply?Supervised Nets [ C ] / / The 18th International Conference on Artificial Intelligence and Statistics. San Diego, California : AISTATS.,2015: 562-570.

[12] Xie S, Tu Z. Holistically?nested edge detection [ C ] / /Proceedings of the IEEE International Conference on Computer Vision. Santiago,Chile:IEEE, 2015: 1395-1403.

[13] Ronneberger O,Fischer P,Brox T. U?net: Convolutional networks for biomedical image segmentation [ C ] / / International Conference on Medical Image Computing and Computer?Assisted Intervention. Berlin: Springer International Publishing, 2015:234-241.

[14] Wu Z, Song S, Khosla A, et al. 3D shapenets: a deep representation for volumetric shapes [ C ] / / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston,MA:IEEE,2015: 1912-1920.

[15] Shattuck DW,Mirza M,Adisetiyo V,et al. Construction of a 3D probabilistic atlas of human cortical structures[ J]. Neuroimage,2008,39(3):1064-1080.

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