Towards Robust Brain Midline Shift Detection: A YOLO-Based 3D Slicer Extension with a Novel Dataset.

Abstract

Accurate detection of brain midline shift is critical for the diagnosis and monitoring of neurological conditions such as traumatic brain injuries, strokes, and tumors. This study aims to address the lack of dedicated datasets and tools for this task by introducing a novel dataset and a 3D Slicer extension, evaluating the effectiveness of multiple deep learning models for automatic detection of brain midline shift. We introduce the brain-midline-detection dataset, specifically designed for identifying three brain landmarks-Anterior Falx (AF), Posterior Falx (PF), and Septum Pellucidum (SP)-in MRI scans. A comprehensive performance evaluation was conducted using deep learning models including YOLOv5 (n, s, m, l), YOLOv8, and YOLOv9 (GELAN-C model). The best-performing model was integrated into the 3D Slicer platform as a custom extension, incorporating steps such as MRI preprocessing, filtering, skull stripping, registration, and midline shift computation. Among the evaluated models, YOLOv5l achieved the highest precision (0.9601) and recall (0.9489), while YOLOv5m delivered the best mAP@0.5:0.95 score (0.6087). YOLOv5n and YOLOv5s exhibited the lowest loss values, indicating high efficiency. Although YOLOv8s achieved a higher mAP@0.5:0.95 score (0.6382), its high loss values reduced its practical effectiveness. YOLOv9-GELAN-C performed the worst, with the highest losses and lowest overall accuracy. YOLOv5m was selected as the optimal model due to its balanced performance and was successfully integrated into 3D Slicer as an extension for automated midline shift detection. By offering a new annotated dataset, a validated detection pipeline, and open-source tools, this study contributes to more accurate, efficient, and accessible AI-assisted medical imaging for brain midline assessment.