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Abstract #3413

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

Céline Dubroy-McArdle1,2, Wilfred Lam3, Greg Stanisz3,4, and Dafna Sussman1,2,5
1Department of Biomedical Physics and Engineering, Toronto Metropolitan University, Toronto, ON, Canada, 2Institute for Biomedical Engineering, Science and Technology, iBEST, Toronto, ON, Canada, 3Sunnybrook Research Institute, Toronto, ON, Canada, 4Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, 5Obstetrics and Gynecology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Synopsis

Keywords: Segmentation, Machine Learning/Artificial Intelligence, Brain Metastases

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.

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Keywords