Clinical Report: Gradio-CNN for Automated Hypothalamus and ICV Segmentation
Overview
This study presents an improved convolutional neural network (CNN) approach integrated with a Gradio-based graphical user interface (GUI) for automated segmentation of the hypothalamus and intracranial volume (ICV) from MRI data. The method demonstrates robust performance with a five-fold cross-validation strategy and offers a user-friendly platform accessible across devices without installation.
Background
The hypothalamus is a critical brain region involved in regulating hormonal balance, thermoregulation, sleep, and emotional responses. Accurate segmentation of the hypothalamus in MRI is essential for studying its structural changes in neurological diseases such as ALS, Parkinson’s, and Alzheimer’s disease. Manual segmentation is labor-intensive and variable, prompting the development of automated CNN-based methods. However, accessibility and ease of use remain challenges, which this study addresses by integrating segmentation models with a Gradio GUI.
Data Highlights
Parameter
Value
Subjects (ALS patients)
66 (108 MRI volumes)
Subjects (Healthy controls)
42
Mean age (ALS)
61 ± 9 years
Mean age (Controls)
53 ± 17 years
Hypothalamus MRI slices per subject
50 slices, 0.125 × 0.125 × 0.5 mm³ resolution
ICV MRI slices per subject
512 slices, 0.5 × 0.5 × 0.5 mm³ resolution
Test set size
30 subjects (15 ALS, 15 controls)
Cross-validation
Five-fold with 20% validation per fold
Key Findings
The CNN model achieved high segmentation accuracy for hypothalamus and ICV using standardized z-score intensity normalization without data augmentation.
The five-fold cross-validation strategy improved robustness and generalizability compared to previous single-model approaches.
The Gradio-based GUI enables easy, installation-free access to the segmentation pipeline across platforms, facilitating use by non-specialists.
The method supports population-level studies by enabling efficient, reproducible hypothalamic volume quantification normalized to ICV.
Proof-of-concept validation in ALS patients demonstrated practical applicability in detecting hypothalamic atrophy.
Clinical Implications
This automated segmentation approach streamlines hypothalamic volume analysis, reducing reliance on expert manual delineation and minimizing variability. The accessible GUI promotes broader adoption in clinical and research settings, enabling large-scale studies of hypothalamic involvement in neurological and metabolic disorders. Normalization to ICV further enhances the reliability of volumetric assessments.
Conclusion
The integration of a CNN-based segmentation model with a Gradio GUI provides an effective, user-friendly tool for accurate hypothalamus and ICV segmentation from MRI data. This approach facilitates reproducible volumetric analyses critical for advancing research and clinical evaluation of hypothalamic pathology.
References
Previous Work on AI-Assisted Hypothalamic Atrophy Quantification, 2023 -- CNN-Based Automatic Segmentation
