脑卒中脑损伤的人工智能分割与分类方法

IF 1.3 Q4 ENGINEERING, BIOMEDICAL
Roberto Mena, Enrique Pelaez, Francis Loayza, Alex Macas, Heydy Franco-Maldonado
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引用次数: 0

摘要

摘要中风引起的脑损伤是全球致残的主要原因之一。目前的治疗方法需要专门的医生在诊断和决定具体的治疗方法之前分析核磁共振成像图像。然而,这个过程既昂贵又耗时。人工智能技术正在成为分析核磁共振成像图像的游戏规则改变者。这项工作提出了一种分三个阶段的端到端方法:将图像归一化到标准MNI空间的预处理技术,以及不均匀性和偏差校正;利用CNN网络进行病变分割,训练脑血管意外并提取特征;并且,用于确定病变发生的血管区域的分类。使用cci - net进行脑卒中分割。评估了四种深度学习(DL)和四种浅机器学习(ML)网络架构,以评估笔画的区域定位。所有模型的架构都是根据它们的性能分数进行设计、分析和比较的,深度学习模型的准确率达到84%,浅层机器学习模型的准确率达到95%。提出的方法可能有助于快速和准确的中风评估急性治疗,以尽量减少患者的并发症。感谢Carlos Jimenez, Alisson Constantine和Edwin Valarezo在完善文本和调试脚本方面所做的贡献。所有作者均已阅读并同意稿件内容;没有任何经济利益需要报告,或申报任何利益冲突,也没有涉及资金来源。我们保证提交的作品是原创作品,没有被任何其他出版物审查过。罗伯托·亚历杭德罗·梅纳是Escuela Superior politcnica del Litoral - ESPOL大学计算机科学工程专业的研究生。在他的职业生涯中,他作为数据分析师在各种研究项目中发挥了主导作用,主要集中在磁共振成像(MRI)处理和可视化的系统开发上。恩里克PelaezDr。Enrique Peláez于1994年在美国南卡罗莱纳大学获得计算机工程博士学位。目前,他是ESPOL大学的教授,领导计算智能领域的人工智能研究。近年来,Pelaez博士一直从事帕金森病的应用研究,利用机器和深度学习技术。他的学术贡献在主要出版物和论坛上展示,并在几个会议和专题讨论会上发表了论文。Pelaez博士的研究成果已发表在IEEE和Nature Communications等期刊上。他的研究课题包括脑电图信号分类、医学成像的深度学习和使用人工智能的行为信号处理。弗朗西斯LoayzaDr。Francis Loayza是ESPOL大学机械工程系(FIMCP)的全职教授。他于2010年获得西班牙纳瓦拉大学神经科学博士学位。Loayza博士在图像数据分析方面拥有深厚的专业知识,他利用功能性磁共振成像和基于体素的形态测量学等统计方法。此外,他对机器和深度学习方法的应用为神经退行性疾病的知识增长做出了贡献。Alex Macas Alcocer是Escuela Superior politcima del Litoral - ESPOL大学计算机科学工程专业的研究生。他一直是一名数据科学家,使用人工智能技术分析磁共振图像,以及网络开发。Heydy Franco-MaldonadoDr。Heydy Franco Maldonado是Imagenología的杰出专家,曾在昆卡大学接受培训。她在墨西哥国立自治大学攻读了磁共振专业,然后在巴塞罗那大学获得了乳腺病理成像文凭。目前,她在瓜亚基尔的Luis Vernaza医院和厄瓜多尔瓜亚基尔SOLCA担任医学放射科医生。除了她的临床角色,Maldonado博士还是ESPOL人工智能研究小组的积极成员,并与Luis Vernaza医院一起协调Espíritu Santo大学Imagenología的研究生课程。此外,她是拜耳公司公认的演讲者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An artificial intelligence approach for segmenting and classifying brain lesions caused by stroke
ABSTRACTBrain injuries caused by strokes are one of the leading causes of disability worldwide. Current procedures require a specialised physician to analyse MRI images before diagnosing and deciding on the specific treatment. However, the procedure can be costly and time-consuming. Artificial intelligence techniques are becoming a game-changer for analysing MRI images. This work proposes an end-to-end approach in three stages: Pre-processing techniques for normalising the images to the standard MNI space, as well as inhomogeneities and bias corrections; lesion segmentation using a CNN network, trained for cerebrovascular accidents and feature extraction; and, classification for determining the vascular territory within which the lesion occurred. A CLCI-Net was used for stroke segmentation. Four Deep Learning (DL) and four Shallow Machine Learning (ML) network architectures were evaluated to assess the strokes’ territory localisation. All models’ architectures were designed, analysed, and compared based on their performance scores, reaching an accuracy of 84% with the DL models and 95% with the Shallow ML models. The proposed methodology may be helpful for rapid and accurate stroke assessment for an acute treatment to minimise patient complications.KEYWORDS: Artificial intelligencelesion segmentationMRI preprocessingstroke assessment AcknowledgementWe would like to thank Carlos Jimenez, Alisson Constantine and Edwin Valarezo for their helpful contribution in perfecting the text and debugging the scripts.Disclosure statementAll authors have seen and agreed with the content of the manuscript; there is no financial interest to report, or declare any conflicts of interest, neither there are funding sources involved. We certify that the submission is original work and is not under review at any other publication.Additional informationNotes on contributorsRoberto MenaRoberto Alejandro Mena is a graduate student in Computer Science Engineering from Escuela Superior Politécnica del Litoral – ESPOL University. Throughout his career, he has played a leading role as a data analyst in various research projects, mainly centered on system development for magnetic resonance imaging (MRI) processing and visualization.Enrique PelaezDr. Enrique Peláez earned his Ph.D. in Computer Engineering from the University of South Carolina, USA, in 1994. Currently, he is a Professor at ESPOL University where he leads the AI research in Computational Intelligence. Over recent years, Dr. Pelaez has been engaged in applied research on Parkinson's Disease, leveraging machine and deep learning techniques. His academic contributions showcased in leading publications and forums, with papers presented in several conferences and symposia. Dr. Pelaez's work has been published in journals, including the IEEE and Nature Communications. His research topics encompass EEG signal classification, deep learning for medical imaging, and behavioral signal processing using AI.Francis LoayzaDr. Francis Loayza serves as a Full Professor in the Mechanical Engineering Department (FIMCP) at ESPOL University. He was conferred with a Ph.D. in Neurosciences from the University of Navarra, Spain, in 2010. With a deep-rooted expertise in image data analysis, Dr. Loayza utilizes statistical methods such as functional Magnetic Resonance Imaging and Voxel Based Morphometry. Furthermore, his application of machine and deep learning methodologies is contributing to the growing knowledge of neurodegenerative disorders.Alex MacasAlex Macas Alcocer is a graduate student in Computer Science Engineering from Escuela Superior Politécnica del Litoral – ESPOL University. He has been working as a Data Scientist, analyzing magnetic resonance images using artificial intelligence techniques, as well as in web development.Heydy Franco-MaldonadoDr. Heydy Franco Maldonado is a distinguished specialist in Imagenología, trained at the Universidad de Cuenca. She further advanced her specialization in Magnetic Resonance at UNAM, Mexico, then pursued a diploma in Breast Pathology Imaging from the Universidad de Barcelona. Presently, she serves as a Medical Radiologist at both Hospital Luis Vernaza in Guayaquil and SOLCA Guayaquil - Ecuador. Besides her clinical roles, Dr. Maldonado is an active member of ESPOL's Artificial Intelligence Research Group and coordinates the postgraduate program in Imagenología at Universidad Espíritu Santo in conjunction with Hospital Luis Vernaza. Additionally, she is a recognized speaker for Bayer.
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来源期刊
CiteScore
2.80
自引率
6.20%
发文量
102
期刊介绍: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.
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