基于人工智能的未破裂颅内动脉瘤形态学指标三维测量方法

doi:10.3969/j.issn.1673-5765.2024.01.015

摘要/Abstract

摘要： 未破裂颅内动脉瘤形态学分析被广泛用于动脉瘤破裂的风险评估和术前规划。三维视角较既往的二维视角更能精确体现动脉瘤的形态变化，但人工测量在三维视角下准确度较低且可重复性差，而人工智能在三维视角下的测量更有优势。但是，目前尚缺乏人工智能在三维视角下的动脉瘤形态测量方法。考虑到三维视角下的规范化测量对形态学参数的临床和科研运用具有重要意义，本文提出了一套三维视角下基于人工智能的未破裂颅内动脉瘤形态学指标测量方法，以期推动动脉瘤研究的同质化和临床诊疗的规范化发展。

文章导读： 本文提出了基于人工智能的未破裂颅内动脉瘤形态学指标三维分析方法，有助于推动动脉瘤研究的同质化和临床诊疗的规范化发展。

关键词: 颅内动脉瘤; 形态学指标; 人工智能; 三维

Abstract: Morphological analysis of unruptured intracranial aneurysms is widely used in risk assessment and surgical design. Compared with the manual measurement in the two-dimensional perspective, the three-dimensional perspective can reflect the morphology changes of aneurysms more accurately. But the manual measurement in the three-dimensional perspective has poor accuracy and repeatability. Therefore, the measurement of artificial intelligence in the three-dimensional perspective has greater advantages than that of manual measurement. However, the method of aneurysm morphological measurement in the three-dimensional perspective based on artificial intelligence has not been proposed. The standardized measurement in the three-dimensional perspective is of great significance to the clinical and research application of morphological parameters. In this paper, a three-dimensional measurement method for unruptured intracranial aneurysms based on artificial intelligence is presented, in order to promote the homogenization of aneurysm research and the standardization of clinical diagnosis and treatment.

Key words: Intracranial aneurysm; Morphological indicator; Artificial intelligence; Three-dimension

中图分类号:

杨溢, 刘清源, 刘伟奇, 王硕. 基于人工智能的未破裂颅内动脉瘤形态学指标三维测量方法[J]. 中国卒中杂志, 2024, 19(1): 112-119.

YANG Yi, LIU Qingyuan, LIU Weiqi, WANG Shuo. A Three-Dimensional Measurement Method of Morphological Indicators of Unruptured Intracranial Aneurysm Based on Artificial Intelligence[J]. Chinese Journal of Stroke, 2024, 19(1): 112-119.

参考文献

[1] LI M H，CHEN S W，LI Y D，et al. Prevalence of unruptured cerebral aneurysms in Chinese adults aged 35 to 75 years：a cross-sectional study[J]. Ann Intern Med，2013，159（8）：514-521.
[2] MORITA A，KIRINO T，HASHI K，et al. The natural course of unruptured cerebral aneurysms in a Japanese cohort[J]. N Engl J Med，2012，366（26）：2474-2482.
[3] NIEUWKAMP D J，SETZ L E，ALGRA A，et al. Changes in case fatality of aneurysmal subarachnoid haemorrhage over time，according to age，sex，and region：a meta-analysis[J]. Lancet Neurol，2009，8（7）：635-642.
[4] ALGRA A M，LINDGREN A，VERGOUWEN M D I，et al. Procedural clinical complications，case-fatality risks，and risk factors in endovascular and neurosurgical treatment of unruptured intracranial aneurysms：a systematic review and meta-analysis[J]. JAMA Neurol，2019，76（3）：282-293.
[5] BACKES D，RINKEL G J E，GREVING J P，et al. ELAPSS score for prediction of risk of growth of unruptured intracranial aneurysms[J]. Neurology，2017，88（17）：1600-1606.
[6] DHAR S，TREMMEL M，MOCCO J，et al. Morphology parameters for intracranial aneurysm rupture risk assessment[J]. Neurosurgery，2008，63（2）：185-197.
[7] RYU C W，KWON O K，KOH J S，et al. Analysis of aneurysm rupture in relation to the geometric indices：aspect ratio，volume，and volume-to-neck ratio[J]. Neuroradiology，2011，53（11）：883-889.
[8] MOCCO J，BROWN R D，Jr，TORNER J C，et al. Aneurysm morphology and prediction of rupture：an international study of unruptured intracranial aneurysms analysis[J]. Neurosurgery，2018，82（4）：491-496.
[9] VAN DER KAMP L T，RINKEL G J E，VERBAAN D，et al. Risk of rupture after intracranial aneurysm growth[J]. JAMA Neurol，2021，78（10）：1228-1235.
[10] THOMPSON B G，BROWN R D，Jr，AMIN-HANJANI S，et al. Guidelines for the management of patients with unruptured intracranial aneurysms：a guideline for healthcare professionals from the American Heart Association/American Stroke Association[J]. Stroke，2015，46（8）：2368-2400.
[11] GREVING J P，WERMER M J H，BROWN R D，Jr，et al. Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms：a pooled analysis of six prospective cohort studies[J]. Lancet Neurol，2014，13（1）：59-66.
[12] GENG J W，HU P，JI Z，et al. Accuracy and reliability of computer-assisted semi-automated morphological analysis of intracranial aneurysms：an experimental study with digital phantoms and clinical aneurysm cases[J]. Int J Comput Assist Radiol Surg，2020，15（10）：1749-1759.
[13] MALDANER N，STIENEN M N，BIJLENGA P，et al. Interrater agreement in the radiologic characterization of ruptured intracranial aneurysms based on computed tomography angiography[J/OL]. World Neurosurg，2017，103：876-882，e1[2023-09-20]. https://doi.org/10.1016/j.wneu.2017.04.131.
[14] TIMMINS K M，KUIJF H J，VERGOUWEN M D I，et al. Reliability and agreement of 2D and 3D measurements on MRAs for growth assessment of unruptured intracranial aneurysms[J]. AJNR Am J Neuroradiol，2021，42（9）：1598-1603.
[15] ŻYŁKOWSKI J，KUNERT P，JAWORSKI M，et al. Changes of size and shape of small，unruptured intracranial aneurysms in repeated computed tomography angiography studies[J]. Wideochir Inne Tech Maloinwazyjne，2015，10（2）：178-188.
[16] TIMMINS K M，KUIJF H J，VERGOUWEN M D I，et al. Relationship between 3D morphologic change and 2D and 3D growth of unruptured intracranial aneurysms[J]. AJNR Am J Neuroradiol，2022，43（3）：416-421.
[17] LIU J J，CHEN Y C，LAN L，et al. Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network[J]. Eur Radiol，2018，28（8）：3268-3275.
[18] HEO J，PARK S J，KANG S H，et al. Prediction of intracranial aneurysm risk using machine learning
[J/OL]. Sci Rep，2020，10（1）：6921[2023-09-28]. https://doi.org/10.1038/s41598-020-63906-8.
[19] WEI X，JIANG J，CAO W T，et al. Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography
[J/OL]. Eur J Radiol，2022，149：110169[2023-09-21].
https://doi.org/10.1016/j.ejrad.2022.110169.
[20] PARK A，CHUTE C，RAJPURKAR P，et al. Deep learning-assisted diagnosis of cerebral aneurysms using the HeadXNet model[J/OL]. JAMA Netw Open，2019，2（6）：e195600[2023-09-29]. https://doi.org/10.1001/jamanetworkopen.2019.5600.
[21] SICHTERMANN T，FARON A，SIJBEN R，et al. Deep learning-based detection of intracranial aneurysms in 3D TOF-MRA[J]. AJNR Am J Neuroradiol，2019，40（1）：25-32.
[22] RAJABZADEH-OGHAZ H，VARBLE N，SHALLWANI H，et al. Computer-assisted three-dimensional morphology evaluation of intracranial aneurysms[J/OL]. World Neurosurg，2018，119：e541-e550[2023-09-11].
https://doi.org/10.1016/j.wneu.2018.07.208.
[23] CHAN S H，WONG K S，WOO Y M，et al. Volume measurement of the intracranial aneurysm：a discussion and comparison of the alternatives to manual segmentation[J]. J Cerebrovasc Endovasc Neurosurg，2014，16（4）：358-363.
[24] XIANG J P，NATARAJAN S K，TREMMEL M，et al. Hemodynamic-morphologic discriminants for intracranial aneurysm rupture[J]. Stroke，2011，42（1）：144-152.
[25] JIANG H，SHEN J，WENG Y X，et al. Morphology parameters for mirror posterior communicating artery aneurysm rupture risk assessment[J]. Neurol Med Chir（Tokyo），2015，55（6）：498-504.
[26] KANG H B，JI W J，QIAN Z H，et al. Aneurysm characteristics associated with the rupture risk of intracranial aneurysms：a self-controlled study
[J/OL]. PLoS One，2015，10（11）：e0142330[2023-09-15]. https://doi.org/10.1371/journal.pone.0142330.
[27] ZHENG Y T，XU F，REN J M，et al. Assessment of intracranial aneurysm rupture based on morphology parameters and anatomical locations[J]. J Neurointerv Surg，2016，8（12）：1240-1246.
[28] DUAN Z H，LI Y H，GUAN S，et al. Morphological parameters and anatomical locations associated with rupture status of small intracranial aneurysms[J/OL]. Sci Rep，2018，8（1）：6440[2023-09-11]. https://doi.org/10.1038/s41598-018-24732-1.
[29] ZHANG J，CAN A，LAI P M R，et al. Age and morphology of posterior communicating artery aneurysms[J/OL]. Sci Rep，2020，10（1）：11545[2023-09-11]. https://doi.org/10.1038/s41598-020-68276-9.
[30] WIEBERS D O，WHISNANT J P，HUSTON J，3rd，et al. Unruptured intracranial aneurysms：natural history，clinical outcome，and risks of surgical and endovascular treatment[J]. Lancet，2003，362（9378）：103-110.
[31] TAWK R G，HASAN T F，D’SOUZA C E，et al. Diagnosis and treatment of unruptured intracranial aneurysms and aneurysmal subarachnoid hemorrhage[J]. Mayo Clin Proc，2021，96（7）：1970-2000.
[32] JIANG P J，LIU Q Y，WU J，et al. A novel scoring system for rupture risk stratification of intracranial aneurysms：a hemodynamic and morphological study[J/OL]. Front Neurosci，2018，12：596[2023-09-11]. https://doi.org/10.3389/fnins.2018.00596.
[33] CORNELISSEN B M W，SCHNEIDERS J J，SPRENGERS M E，et al. Aneurysmal parent artery-specific inflow conditions for complete and incomplete circle of Willis configurations[J]. AJNR Am J Neuroradiol，2018，39（5）：910-915.
[34] GHOLAMPOUR S，MEHRJOO S. Effect of bifurcation in the hemodynamic changes and rupture risk of small intracranial aneurysm[J]. Neurosurg Rev，2021，44（3）：1703-1712.
[35] PICCINELLI M，BACIGALUPPI S，BOCCARDI E，et al. Geometry of the internal carotid artery and recurrent patterns in location，orientation，and rupture status of lateral aneurysms：an image-based computational study[J]. Neurosurgery，2011，68（5）：1270-1285.
[36] SADATOMO T，YUKI K，MIGITA K，et al. Evaluation of relation among aneurysmal neck，parent artery，and daughter arteries in middle cerebral artery aneurysms，by three-dimensional digital subtraction angiography[J]. Neurosurg Rev，2005，28（3）：196-200.
[37] TATESHIMA S，CHIEN A，SAYRE J，et al. The effect of aneurysm geometry on the intra-aneurysmal flow condition[J]. Neuroradiology，2010，52（12）：1135-1141.
[38] SUGIYAMA S，NIIZUMA K，NAKAYAMA T，et al. Relative residence time prolongation in intracranial aneurysms：a possible association with atherosclerosis[J]. Neurosurgery，2013，73（5）：767-776.
[39] UJIIE H，TACHIBANA H，HIRAMATSU O，et al. Effects of size and shape（aspect ratio）on the hemodynamics of saccular aneurysms：a possible index for surgical treatment of intracranial aneurysms[J]. Neurosurgery，1999，45（1）：119-130.
[40] NADER-SEPAHI A，CASIMIRO M，SEN J，et al. Is aspect ratio a reliable predictor of intracranial aneurysm rupture?[J]. Neurosurgery，2004，54（6）：1343-1348.
[41] LAURIC A，GREIM-KUCZEWSKI K，ANTONOV A，et al. Proximal parent vessel tapering is associated with aneurysm at the middle cerebral artery bifurcation[J]. Neurosurgery，2019，84（5）：1082-1089.
[42] FENG X，QI P，WANG L J，et al. Relationship between cerebrovascular atherosclerotic stenosis and rupture risk of unruptured intracranial aneurysm：a single-center retrospective study[J/OL]. Clin Neurol Neurosurg，2019，186：105543[2023-09-11]. https://doi.org/10.1016/j.clineuro.2019.105543.
[43] ANTONOV A，KONO K，GREIM-KUCZEWSKI K，et al. Proximal stenosis is associated with rupture status in middle cerebral artery aneurysms[J/OL]. World Neurosurg，2018，109：e835-e844[2023-09-11]. https://doi.org/10.1016/j.wneu.2017.10.108.
[44] RAHMAN M，SMIETANA J，HAUCK E，et al. Size ratio correlates with intracranial aneurysm rupture status：a prospective study[J]. Stroke，2010，41（5）：916-920.
[45] RAGHAVAN M L，MA B，HARBAUGH R E. Quantified aneurysm shape and rupture risk[J]. J Neurosurg，2005，102（2）：355-362.
[46] JUCHLER N，SCHILLING S，BIJLENGA P，et al. Shape trumps size：image-based morphological analysis reveals that the 3D shape discriminates intracranial aneurysm disease status better than aneurysm size[J/OL]. Front Neurol，2022，13：809391[2023-09-11]. https://doi.org/10.3389/fneur. 2022.809391.
[47] MALIK K M，ANJUM S M，SOLTANIAN-ZADEH H，et al. A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms
[J/OL]. IEEE Access，2018，6：7970-7986[2023-09-11]. https://doi.org/10.1109/ACCESS.2018.2799307.
[48] PICCINELLI M，STEINMAN D A，HOI Y，et al. Automatic neck plane detection and 3D geometric characterization of aneurysmal sacs[J]. Ann Biomed Eng，2012，40（10）：2188-2211.
[49] CÁRDENES R，POZO J M，BOGUNOVIC H，et al. Automatic aneurysm neck detection using surface Voronoi diagrams[J]. IEEE Trans Med Imaging，2011，30（10）：1863-1876.
[50] LARRABIDE I，CRUZ VILLA-URIOL M，CÁRDENES R，et al. Three-dimensional morphological analysis of intracranial aneurysms：a fully automated method for aneurysm sac isolation and quantification[J]. Med Phys，2011，38（5）：2439-2449.
[51] JERMAN T，PERNUŠ F，LIKAR B，et al. Automatic cutting plane identification for computer-aided analysis of intracranial aneurysms[EB/OL]//2016 23rd International Conference on Pattern Recognition（ICPR）. Cancún，México：ICPR，2016：1484-1489.
[52] FORD M D，HOI Y，PICCINELLI M，et al. An objective approach to digital removal of saccular aneurysms：technique and applications[J/OL]. Br J Radiol，2009，82 Spec No 1：S55-S61[2023-09-11]. https://doi.org/10.1259/bjr/67593727.
[53] MERRITT W C，BERNS H F，DUCRUET A F，et al. Definitions of intracranial aneurysm size and morphology：a call for standardization[J/OL]. Surg Neurol Int，2021，12：506[2023-09-11]. https://doi.org/10.25259/SNI_576_2021.
[54] LIU Q L，JIANG P，JIANG Y H，et al. Prediction of aneurysm stability using a machine learning model based on pyradiomics-derived morphological features[J]. Stroke，2019，50（9）：2314-2321.
[55] LIU Q Y，YANG Y，YANG J H，et al. Rebleeding of ruptured intracranial aneurysm after admission：a multidimensional Nomogram model to risk assessment[J/OL]. Front Aging Neurosci，2021，13：692615[2023-09-11]. https://doi.org/10.3389/fnagi.2021.692615.
[56] LV N，KARMONIK C，CHEN S Y，et al. Wall enhancement，hemodynamics，and morphology in unruptured intracranial aneurysms with high rupture risk[J]. Transl Stroke Res，2020，11（5）：882-889.
[57] MU N，LYU Z H，REZAEITALESHMAHALLEH M，et al. An attention residual u-net with differential preprocessing and geometric postprocessing：learning how to segment vasculature including intracranial aneurysms[J/OL]. Med Image Anal，2023，84：102697[2023-09-11]. https://doi.org/10.1016/j.media. 2022.102697.