深度学习在脑小血管病影像学标志物中的研究进展

doi:10.3969/j.issn.1672-6731.2023.01.003

摘要/Abstract

摘要： 随着人工智能技术的飞速发展，特别是深度学习算法的应用，使脑小血管病典型影像学标志物的检测及量化评估速度增快、准确性提高。本文拟综述深度学习算法在脑微出血、脑白质高信号、扩大的血管周围间隙、腔隙、近期皮质下梗死及脑萎缩等脑小血管病影像学标志物中的研究进展，以为脑小血管病的精准医疗提供支持。

关键词: 大脑小血管疾病, 深度学习, 综述

Abstract: With the rapid development of artificial intelligence (AI) technology, especially the application of deep learning (DL), the detection and quantitative evaluation of typical imaging markers of small cerebral vascular disease (CSVD) has been accelerated and the accuracy has been improved. In recent years, it has attracted much attention in the field of medical imaging. This paper intends to summarize the research progress and problems of deep learning in the imaging markers of CSVD such as cerebral microbleeds (CMBs), white matter hyperintensities (WMH), enlarged perivascular space (EPVS), lacunes, recent small subcortical infarcts (RSSI) and cerebral atrophy, so as to provide support for the precise treatment of CSVD.

Key words: Cerebral small vessel diseases, Deep learning, Review

白雪冬, 张小雷, 夏爽. 深度学习在脑小血管病影像学标志物中的研究进展[J]. 中国现代神经疾病杂志, 2023, 23(1): 9-14.

BAI Xue-dong, ZHANG Xiao-lei, XIA Shuang. Progress of deep learning in cerebral small vessel disease imaging markers[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2023, 23(1): 9-14.

https://journal11.magtechjournal.com/cjcnn/CN/Y2023/V23/I1/9

参考文献

[1] Murray NM, Unberath M, Hager GD, Hui FK. Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions:a systematic review[J]. J Neurointerv Surg, 2020, 12:156-164.
[2] Gorelick PB, Scuteri A, Black SE, Decarli C, Greenberg SM, Iadecola C, Launer LJ, Laurent S, Lopez OL, Nyenhuis D, Petersen RC, Schneider JA, Tzourio C, Arnett DK, Bennett DA, Chui HC, Higashida RT, Lindquist R, Nilsson PM, Roman GC, Sellke FW, Seshadri S; American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia. Vascular contributions to cognitive impairment and dementia:a statement for healthcare professionals from the American Heart Association/American Stroke Association[J]. Stroke, 2011, 42:2672-2713.
[3] Nannoni S, Ohlmeier L, Brown RB, Morris RG, MacKinnon AD, Markus HS; DNA Lacunar 2 investigators. Cognitive impact of cerebral microbleeds in patients with symptomatic small vessel disease[J]. Int J Stroke, 2022, 17:415-424.
[4] Ahmedt-Aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. A survey on graph-based deep learning for computational histopathology[J]. Comput Med Imaging Graph, 2022, 95:102027.
[5] Aljabri M, AlAmir M, AlGhamdi M, Abdel-Mottaleb M, Collado-Mesa F. Towards a better understanding of annotation tools for medical imaging:a survey[J]. Multimed Tools Appl, 2022, 81:25877-25911.
[6] Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning:concepts, CNN architectures, challenges, applications, future directions[J]. J Big Data, 2021, 8:53.
[7] Guan H, Liu M. Domain adaptation for medical image analysis:a survey[J]. IEEE Trans Biomed Eng, 2022, 69:1173-1185.
[8] Zhao X, Zhao XM. Deep learning of brain magnetic resonance images:a brief review[J]. Methods, 2021, 192:131-140.
[9] Charidimou A, Krishnan A, Werring DJ, Rolf Jäger H. Cerebral microbleeds:a guide to detection and clinical relevance in different disease settings[J]. Neuroradiology, 2013, 55:655-674.
[10] Al-Masni MA, Kim WR, Kim EY, Noh Y, Kim DH. Automated detection of cerebral microbleeds in MR images:a two-stage deep learning approach[J]. Neuroimage Clin, 2020, 28:102464.
[11] Dou Q, Chen H, Yu L, Zhao L, Qin J, Wang DF, Mok VC, Shi L, Heng PA. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks[J]. IEEE Trans Med Imaging, 2016, 35:1182-1195.
[12] Liu S, Utriainen D, Chai C, Chen Y, Wang L, Sethi SK, Xia S, Haacke EM. Cerebral microbleed detection using susceptibility weighted imaging and deep learning[J]. Neuroimage, 2019, 198:271-282.
[13] Al-Masni MA, Kim WR, Kim EY, Noh Y, Kim DH. A two cascaded network integrating regional-based YOLO and 3D-CNN for cerebral microbleeds detection[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020:1055-1058.
[14] Rashid T, Abdulkadir A, Nasrallah IM, Ware JB, Liu H, Spincemaille P, Romero JR, Bryan RN, Heckbert SR, Habes M. DEEPMIR:a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI[J]. Sci Rep, 2021, 11:14124.
[15] Shi Y, Zhao Z, Tang H, Huang S. Intellectual structure and emerging trends of white matter hyperintensity studies:a bibliometric analysis from 2012 to 2021[J]. Front Neurosci, 2022, 16:866312.
[16] Li XX, Wang XX, Cheng J, Xu H, Li ZX, Liu T. White matter hyperintensities segmentation using neural network ensembles[J]. Zhongguo Zu Zhong Za Zhi, 2020, 15:234-242. 李鑫鑫, 汪绪先, 程健, 徐红, 李子孝, 刘涛. 基于多网络集成的脑白质高信号分割方法[J]. 中国卒中杂志, 2020, 15:234-242.
[17] Ghafoorian M, Karssemeijer N, Heskes T, van Uden IWM, Sanchez CI, Litjens G, de Leeuw FE, van Ginneken B, Marchiori E, Platel B. Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities[J]. Sci Rep, 2017, 7:5110.
[18] Sundaresan V, Zamboni G, Rothwell PM, Jenkinson M, Griffanti L. Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images[J]. Med Image Anal, 2021, 73:102184.
[19] Sundaresan V, Zamboni G, Dinsdale NK, Rothwell PM, Griffanti L, Jenkinson M. Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images[J]. Med Image Anal, 2021, 74:102215.
[20] Rachmadi MF, Valdés-Hernández MDC, Makin S, Wardlaw J, Komura T. Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks[J]. Med Image Anal, 2020, 63:101712.
[21] Ballerini L, Lovreglio R, Valdés Hernández MDC, Gonzalez-Castro V, Maniega SM, Pellegrini E, Bastin ME, Deary IJ, Wardlaw JM. Application of the ordered logit model to optimising Frangi filter parameters for segmentation of perivascular spaces[J]. Procedia Comput Sci, 2016, 90:61-67.
[22] González-Castro V, Valdés Hernández MDC, Chappell FM, Armitage PA, Makin S, Wardlaw JM. Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance[J]. Clin Sci (Lond), 2017, 131:1465-1481.
[23] Dubost F, Adams H, Bortsova G, Ikram MA, Niessen W, Vernooij M, de Bruijne M. 3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI[J]. Med Image Anal, 2019, 51:89-100.
[24] Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, Lindley RI, O'Brien JT, Barkhof F, Benavente OR, Black SE, Brayne C, Breteler M, Chabriat H, Decarli C, de Leeuw FE, Doubal F, Duering M, Fox NC, Greenberg S, Hachinski V, Kilimann I, Mok V, Oostenbrugge RV, Pantoni L, Speck O, Stephan BC, Teipel S, Viswanathan A, Werring D, Chen C, Smith C, van Buchem M, Norrving B, Gorelick PB, Dichgans M; STandards for Reporting Vascular changes on neuroimaging (STRIVE v1). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration[J]. Lancet Neurol, 2013, 12:822-838.
[25] Uchiyama Y, Yokoyama R, Ando H, Asano T, Kato H, Yamakawa H, Yamakawa H, Hara T, Iwama T, Hoshi H, Fujita H. Computer-aided diagnosis scheme for detection of lacunar infarcts on MR images[J]. Acad Radiol, 2007, 14:1554-1561.
[26] Uchiyama Y, Abe A, Muramatsu C, Hara T, Shiraishi J, Fujita H. Eigenspace template matching for detection of lacunar infarcts on MR images[J]. J Digit Imaging, 2015, 28:116-122.
[27] Ghafoorian M, Karssemeijer N, Heskes T, Bergkamp M, Wissink J, Obels J, Keizer K, de Leeuw FE, Ginneken BV, Marchiori E, Platel B. Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin[J]. Neuroimage Clin, 2017, 14:391-399.
[28] Ma C, Li H, Zhang K, Gao Y, Yang L. Risk factors of restroke in patients with lacunar cerebral infarction using magnetic resonance imaging image features under deep learning algorithm[J]. Contrast Media Mol Imaging, 2021:ID2527595.
[29] Finck T, Schinz D, Grundl L, Eisawy R, Yiğitsoy M, Moosbauer J, Zimmer C, Pfister F, Wiestler B. Automated detection of ischemic stroke and subsequent patient triage in routinely acquired head CT[J]. Clin Neuroradiol, 2022, 32:419-426.
[30] Hou Y, Liu Q, Chen J, Wu B, Zeng F, Yang Z, Song H, Liu Y. Application value of T2 fluid-attenuated inversion recovery sequence based on deep learning in static lacunar infarction[J]. Acta Radiol, 2022.[Epub ahead of print]
[31] Ozzoude M, Ramirez J, Raamana PR, Holmes MF, Walker K, Scott CJM, Gao F, Goubran M, Kwan D, Tartaglia MC, Beaton D, Saposnik G, Hassan A, Lawrence-Dewar J, Dowlatshahi D, Strother SC, Symons S, Bartha R, Swartz RH, Black SE. Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions[J]. Front Neurosci, 2020, 14:598868.
[32] Rebsamen M, Suter Y, Wiest R, Reyes M, Rummel C. Brain morphometry estimation:from hours to seconds using deep learning[J]. Front Neurol, 2020, 11:244.
[33] Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification[J]. Neurocomput, 2018, 321:321-331.
[34] Bernal J, Valverde S, Kushibar K, Cabezas M, Oliver A, Lladó X; Alzheimer's Disease Neuroimaging Initiative. Generating longitudinal atrophy evaluation datasets on brain magnetic resonance images using convolutional neural networks and segmentation priors[J]. Neuroinformatics, 2021, 19:477-492.
[35] Gregory J, Welliver S, Chong J. Top 10 reviewer critiques of radiology artificial intelligence (AI) articles:qualitative thematic analysis of reviewer critiques of machine learning/deep learning manuscripts submitted to JMRI[J]. J Magn Reson Imaging, 2020, 52:248-254.

选择文件类型/文献管理软件名称

选择包含的内容

2 深度学习在脑小血管病影像学标志物中的研究进展

Progress of deep learning in cerebral small vessel disease imaging markers

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	佟志勇. 颅内动脉瘤与脑血管重建术[J]. 中国现代神经疾病杂志, 2024, 24(8): 595-598.
[2]	佟志勇. 脑血管重建术历史沿革[J]. 中国现代神经疾病杂志, 2024, 24(8): 599-605.
[3]	金凌骥, 胡俊文, 李寅, 王林. 复杂颅内动脉瘤的颅内-颅内血管搭桥术[J]. 中国现代神经疾病杂志, 2024, 24(8): 606-612.
[4]	刘佩玺, 史源, 朱巍. 血流导向时代颅内全域脑血管搭桥术治疗颅内动脉瘤之应用方略[J]. 中国现代神经疾病杂志, 2024, 24(8): 613-618.
[5]	张峰, 孟凡刚. 帕金森病自主神经功能障碍丘脑底核脑深部电刺激术治疗进展[J]. 中国现代神经疾病杂志, 2024, 24(7): 510-515.
[6]	吴桂智, 田宏. Meige综合征脑深部电刺激术治疗进展[J]. 中国现代神经疾病杂志, 2024, 24(7): 516-521.
[7]	孟凡刚, 张建国. 耳后磨骨槽技术在脑深部电刺激术中的应用[J]. 中国现代神经疾病杂志, 2024, 24(7): 522-524.
[8]	陈琳, 潘华. 功能性震颤的电生理诊断[J]. 中国现代神经疾病杂志, 2024, 24(7): 573-578.
[9]	魏俊吉, 常健博. 深入基层医院普及神经外科重症管理理念[J]. 中国现代神经疾病杂志, 2024, 24(6): 403-406.
[10]	史若琳, 高秀洁, 崔家宁, 邵妍, 谢璐霜, 刘毅. 5-羟色胺与帕金森病非运动症状研究进展[J]. 中国现代神经疾病杂志, 2024, 24(6): 497-501.
[11]	李海峰. 在进入靶向治疗的时代激素仍然是重症肌无力治疗的基础[J]. 中国现代神经疾病杂志, 2024, 24(5): 295-307.
[12]	许鸿嘉, 舒崖清, 王蘋, 李蕃, 许顺良, 邱伟, 马晓宇. 抗水通道蛋白4抗体阳性视神经脊髓炎谱系疾病诊断与治疗进展[J]. 中国现代神经疾病杂志, 2024, 24(5): 308-314.
[13]	黄瑀, 廖金池, 舒崖清. 抗N-甲基-D-天冬氨酸受体脑炎睡眠障碍研究进展[J]. 中国现代神经疾病杂志, 2024, 24(5): 315-319.
[14]	刘彩云, 金涛. 树突状细胞在多发性硬化免疫治疗中的应用[J]. 中国现代神经疾病杂志, 2024, 24(5): 320-330.
[15]	王军成, 赵岳阳, 杨强, 蒯涛, 潘亚文. HMGB1基因调控胶质瘤恶性生物学行为研究进展[J]. 中国现代神经疾病杂志, 2024, 24(5): 391-397.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

2 深度学习在脑小血管病影像学标志物中的研究进展

Progress of deep learning in cerebral small vessel disease imaging markers

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价