Automatic Segmentation of Brain Metastasis Sub-Regions using MTRNOE Imaging for Enhanced Tumour Boundary Definition

Motivation: Brain metastasis treatment monitoring requires precise tumour segmentation, but manual methods are time-intensive and prone to variability. Current methods overlook metabolic and microstructural changes, focusing only on macrostructural components. Goal(s): Develop an automatic segmentation model that delineates Gd-enhanced, edema, and Nuclear Overhauser Magnetization Transfer ratio (NOE-MT) attenuated sub-regions, incorporating metabolic and microstructural information for improved boundary definition. Approach: Data from 101 patients consisting of contrast-enhanced T1w, FLAIR, and MTRNOE images, trained a modified U-Net with spatial attention. Performance was evaluated using Dice and boundary F1-scores. Results: The model achieved reliable segmentation, demonstrating strong boundary delineation and potential for treatment monitoring. Impact: Introduces the novel Nuclear Overhauser Magnetization Transfer (NOE-MT) sub-region segmentation, providing metabolic and microstructural tumour insights. An automatic model that accurately delineates brain metastasis sub-regions, enhancing boundary definition and reducing observer variability, with implications for improved treatment planning and monitoring.