ENHANCING GENERALIZABILITY IN BRAIN TUMOR SEGMENTATION: MODEL ENSEMBLE WITH ADAPTIVE POST-PROCESSING.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Accurate brain tumor segmentation in multi-parametric MRI is vital for clinical decision-making, patient prognosis, and personalized care.
• The Brain Tumor Segmentation (BraTS) challenge has expanded to include multiple tumor types, necessitating methods with broad generalizability.
• Existing segmentation methods may struggle with diverse tumor morphologies and characteristics.

Purpose of the Study:

• To develop and evaluate a deep learning-based ensemble strategy for robust brain tumor segmentation.
• To introduce a novel adaptive post-processing method to enhance segmentation accuracy and generalizability across various tumor types.
• To address the challenges posed by the BraTS-GoAT competition, focusing on cross-tumor generalizability.

Main Methods:

• An ensemble of three state-of-the-art deep learning segmentation models was employed.
• A novel adaptive post-processing technique utilizing cross-validated tumor-specific thresholding was developed.
• The method was evaluated on validation datasets for generalizability across different brain tumor types.

Main Results:

• The proposed method achieved lesion-wise Dice scores of 0.842, 0.854, and 0.872 for enhancing tumor, tumor core, and whole tumor, respectively.
• Lesion-wise 95th-percentile Hausdorff Distance scores were 29.46, 24.67, and 25.22 for the respective tumor subregions.
• The results demonstrate strong performance and generalizability across various tumor types.

Conclusions:

• The combined deep learning ensemble and adaptive post-processing approach significantly improves brain tumor segmentation accuracy.
• The method exhibits excellent generalizability across diverse brain tumor types, crucial for clinical applications.
• This work contributes to advancing automated segmentation tools for improved cancer diagnosis and treatment planning.