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

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Intelligent Medicine 2024年第3期4卷 161-169页

作者： puranam Revanth kumar Rajesh kumar Jha p akhendra kumar B Deevena Raju Department of Electronics and Communication Engineering Faculty of Science and TechnologyICFAI Foundation for Higher EducationHyderabadIndia Department of Data Science&AI Faculty of Science and TechnologyICFAI Foundation for Higher EducationHyderabadIndia

Objective Segmentation of medical images is a crucial process in various image analysis *** segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image *** of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging(MRI)*** allows for precise quantitative examination of the brain,which aids in diagnosis,identification,and classification of ***,the efficacy of the segmentation approach is crucial to disease diagnosis and treatment *** 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)*** hybrid optimization algorithm,FrHHGO,integrates the fractional Henry gas optimization(FHGO)and horse herd optimization(HHO)*** required 30 min,whereas testing and segmentation of brain tissues from an unseen image required an average of 12 *** Compared to the results obtained with no refinements,the Skull stripping refinement showed significant *** the method included a preprocessing stage,it was flexible enough to enhance image quality,allowing for better results even with low-resolution *** 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

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