Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging  

作　　者：Puranam Revanth Kumar Rajesh Kumar Jha P Akhendra Kumar B Deevena Raju 

机构地区：[1]Department of Electronics and Communication Engineering,Faculty of Science and Technology,ICFAI Foundation for Higher Education,Hyderabad,India [2]Department of Data Science&AI,Faculty of Science and Technology,ICFAI Foundation for Higher Education,Hyderabad,India

出　　处：《Intelligent Medicine》2024年第3期161-169,共9页智慧医学（英文）

摘　　要：Objective Segmentation of medical images is a crucial process in various image analysis applications.Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis.One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging(MRI)data.It allows for precise quantitative examination of the brain,which aids in diagnosis,identification,and classification of disorders.Consequently,the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.Methods This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans us-ing a fractional Henry horse herd gas optimization-based Shepard convolutional neural network(FrHHGO-based ShCNN).To segment the clinical brain MRI images into white matter(WM),grey matter(GM),and cerebrospinal fluid(CSF)tissues,the proposed framework was evaluated on the Lifespan Human Connectome Projects(HCP)database.The hybrid optimization algorithm,FrHHGO,integrates the fractional Henry gas optimization(FHGO)and horse herd optimization(HHO)algorithms.Training required 30 min,whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.Results Compared to the results obtained with no refinements,the Skull stripping refinement showed significant improvement.As the method included a preprocessing stage,it was flexible enough to enhance image quality,allowing for better results even with low-resolution input.Maximum precision of 93.2%,recall of 91.5%,Dice score of 91.1%,and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN,which was superior to all other approaches.

关 键 词：Brain tissue segmentation Deep learning Magnetic resonance imaging Optimization technique Shepard convolutional neural network 

分 类 号：R73[医药卫生—肿瘤] R445[医药卫生—临床医学]