Automatic Detection for Cerebral Aneurysms in TOF-MRA Images Based on Fuzzy Label and Deep Learning

doi:10.11938/cjmr20223004

Abstract

Abstract:

Subarachnoid hemorrhage caused by the rupture of cerebral aneurysms is extremely fatal and disabling. It’s imperative for radiologists to achieve efficient screening with the help of deep learning-based models. To improve the detection sensitivity of time of flight-magnetic resonance angiography (TOF-MRA) images, this study proposed a neural network named DCAU-Net which is based on fuzzy labels, 3D U-Net variant, and dual-branch channel attention (DCA), and able to adaptively adjust the response of channel features to improve feature extraction capability. First, TOF-MRA images from 260 subjects were preprocessed, and the data were split into the training set (N=174), validation set (N=43) and testing set (N=43). Then the preprocessed data were used for training and validating DCAU-Net. The results show that DCAU-Net scores 90.69% of sensitivity, 0.83 per case of false positive count and 0.52 of positive predicted value in the testing set, providing a promising tool for detecting cerebral aneurysms.

Key words: cerebral aneurysm, automatic detection, deep learning, fuzzy label, dual branch channel attention mechanism

CLC Number:

TP394.1

Meng CHEN, Chen GENG, Yu-xin LI, Dao-ying GENG, Yi-fang BAO, Ya-kang DAI. Automatic Detection for Cerebral Aneurysms in TOF-MRA Images Based on Fuzzy Label and Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2022, 39(3): 267-277.

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References 22

1	邹应诚. 基于卷积神经网络的颅内动脉瘤检测方法研究[D]. 武汉: 华中科技大学, 2019.
2	SUAREZ J I, TARR R W, SELMAN W RAneurysmal subarachnoid hemorrhage[J].New Engl J Med,2006,354(4):387-396. doi: 10.1056/NEJMra052732
3	中国蛛网膜下腔出血诊治指南2019[J]. 中华神经科杂志, 2019, 52(12): 1006-1021.
	Chinese guidelines for diagnosis and treatment of subarachnoid hemorrhage 2019[J]. Chinese Journal of Neurology, 2019, 52(12): 1006-1021.
4	沈瑞乐. 脑动脉瘤形成、破裂与脑血管形态学类型相关性研究[D]. 郑州: 郑州大学, 2016.
5	JOSEPH J J, DONNER T WLong-term insulin glargine therapy in type 2 diabetes mellitus: a focus on cardiovascular outcomes[J].Vasc Health Risk Manag,2015,11,107-116.
6	SONOBE M, YAMAZAKI T, YONEKURA M, et alSmall unruptured intracranial aneurysm verification study: SUAVe study, Japan[J].Stroke,2010,41(9):1969-1977. doi: 10.1161/STROKEAHA.110.585059
7	娄云重, 刘颖, 江华, 等基于MRI和深度学习的桥小脑角区脑膜瘤与听神经瘤分类算法研究[J].波谱学杂志,2020,37(3):300-310.
	LOU Y Z, LIU Y, JIANG H, et alA deep learning algorithm for classifying meningioma and auditory neuroma in the cerebellopontine angle from magnetic resonance images[J].Chinese J Magn Reson,2020,37(3):300-310.
8	SAILER A M, WAGEMANS B A, NELEMANS P J, et alDiagnosing intracranial aneurysms with MR angiography: systematic review and meta-analysis[J].Stroke,2014,45(1):119-126. doi: 10.1161/STROKEAHA.113.003133
9	DAVIS W I, WARNOCK S H, HANSBERGER H RIntracranial MRA: single volume vs. multiple thin slab 3D time-of-flight acquisition[J].J Comput Assist Tomogr,1993,17(1):15-21. doi: 10.1097/00004728-199301000-00002
10	魏志宏, 闫士举, 韩宝三, 等基于多输出的3D卷积神经网络诊断阿尔兹海默病[J].波谱学杂志,2021,38(1):92-100.
	WEI Z H, YAN S J, HAN B S, et alDiagnosis of Alzheimer’s disease based on multi-output three-dimensional convolutional neural network[J].Chinese J Magn Reson,2021,38(1):92-100.
11	NAKAO T, HANAOKA S, NOMURA Y, et al Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography[J]. J Magn Reson Imaging, 2018, 47 (4): 948- 953. doi: 10.1002/jmri.25842
12	SAKAI M, MURAYAMA S, GIBO M, et alCan maximum intensity projection images with multidetector-row computed tomography help to differentiate between the micronodular distribution of focal and diffuse infiltrative lung diseases?[J].J Comput Assist Tomogr,2005,29(5):588-591. doi: 10.1097/01.rct.0000175710.98923.a2
13	UEDA D, YAMAMOTO A, NISHIMORI M, et alDeep learning for MR angiography: Automated detection of cerebral aneurysms[J].Radiology,2019,290(1):187-194. doi: 10.1148/radiol.2018180901
14	HE K M, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: IEEE, 2016: 770-778.
15	SICHTERMANN T, FARON A, SIJBEN R, et alDeep learning-based detection of intracranial aneurysms in 3D TOF-MRA[J].Am J Neuroradiol,2019,40(1):25-32. doi: 10.3174/ajnr.A5911
16	KAMNITSAS K, LEDIG C, NEWCOMBE VFJ, et al Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation[J]. Med Image Anal, 2017, 36, 61- 78. doi: 10.1016/j.media.2016.10.004
17	GENG C, XIA W, HUANG L, et al Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network[J]. Biomed Eng Online, 2020, 19, 38. doi: 10.1186/s12938-020-00770-7
18	ÇIçEK Ö, ABDULKADIR A, LIENKAMP S S, et al. 3D U-Net: Learning dense volumetric segmentation from sparse annotation[C]// Medical Image Computing and Computer-Assisted Intervention, Berlin, Heidelberg. USA: MICCAI, 2016: 424-432.
19	GOYAL H, SANDEEP D, VENU R, et alNormalization of data in data mining[J].International Journal of Software and Web Sciences,2014,10,32-33.
20	DIEDERIK P K, JIMMY B. Adam: A method for stochastic optimization. arXiv preprint arXiv: 1412.6980, 2014.
21	MACYSZYN L, AKBARI H, PISAPIA J M, et alImaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques[J].Neuro-oncolog,2015,18(3):417-425.
22	EGAN J P, GREENBERG G Z, SCHULMAN A IOperating characteristics, signal detectability, and the method of free response[J].J Acoust Soc Am,1961,33,993-1007. doi: 10.1121/1.1908935

特征	训练集	验证集	测试集
患者数量	174	43	43
年龄/岁	61±12	62±10	58±14
男性数量/女性数量	73/101	16/27	14/29
动脉瘤最大径/mm	5.48±3.29	5.42±3.25	5.48±3.82
动脉瘤总数量	182	46	46
颈内动脉区动脉瘤数量	100	25	32
大脑中动脉区动脉瘤数量	26	10	4
大脑前动脉区动脉瘤数量	30	6	7
大脑后动脉区动脉瘤数量	17	1	0
基底动脉区动脉瘤数量	5	3	3
椎动脉区动脉瘤数量	4	1	0

仪器	场强	重复时间/回波时间	视野百分比相位	采集矩阵	翻转角	层厚
Siemens	3 T	21 ms/3.69 ms	58%~90%	(256~384) × (197~331)	18?~25?	0.5~1 mm
GE	1.5 T	33 ms/6.3 ms	75%~100%	(288~384) × 192/195	20?	1.2~1.6 mm
	3 T	25 ms/3.4 ms	70%~94%	(320~384) × 192	15?	1~2.4 mm

方法	Sensitivity/%	FP count/(个/例)	PPV
ResNet-18^[14]	51.16	0.57	0.44
DeepMedic^[15]	86.04	3.40	0.21
3D U-Net^[17]	81.39	1.09	0.39
DCAU-Net（本文方法）	90.69	0.83	0.52

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