Deep learning-based automatic segmentation of brain structures on MRI: A test-retest reproducibility analysis

The aim of our study was to assess the reproducibility of deep learning-based automatic segmentation of brain structures in MRI scans across different scanner types and magnetic field strengths, particularly focusing on the comparison between 1.5 T and 3 T MRI scanners.

Our analysis encompassed a comprehensive examination of MRI images, focusing on the consistency of volumetric segmentation. We utilized advanced deep learning techniques with human-in-the-loop as a part of the workflow for segmenting brain structures and compared results across subsequent scans using the same and different scanner types.

Our findings revealed high consistency in volumetric segmentation when comparing scans conducted on the same type of scanner (1.5 T to 1.5 T or 3 T to 3 T). The study revealed slightly better segmentation results for 1.5 T scanners compared to 3 T scanners when each was used independently. However, cross-comparisons between different scanner types (1.5 T vs. 3 T) demonstrated slightly less consistency, highlighting the influence of magnetic field strength on segmentation accuracy.

This study emphasizes the necessity of using the same scanner type and protocol for reliable MRI studies, particularly for brain atrophy monitoring. The high repeatability of deep learning-based segmentation under these conditions confirms its efficacy for clinical and research applications.