Hypothalamus and intracranial volume segmentation at the group level by use of a Gradio-CNN framework

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Accurate segmentation of the hypothalamus is crucial for understanding its role in various neurological and metabolic disorders.
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Manual segmentation of the hypothalamus is labor-intensive and variable, highlighting the need for automated methods.
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Convolutional neural networks (CNNs) have shown high precision in medical image segmentation, including complex brain structures.
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The Gradio library enables the development of user-friendly interfaces for CNN models, making advanced imaging technology accessible.
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This study presents an improved segmentation methodology and a comprehensive GUI for hypothalamus and intracranial volume analysis.

International Journal Of Computer Assisted Radiology And Surgery

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