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Abstract
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Metastatic brain tumors present significant challenges in diagnosis and treatment, contributing to high mortality rates worldwide. Magnetic resonance imaging (MRI) is a pivotal diagnostic tool for identifying and assessing these tumors. However, accurate segmentation of MRI images remains critical for effective treatment planning and prognosis determination. Traditional segmentation methods, including threshold- based algorithms, often struggle with precisely delineating tumor boundaries, especially in three- dimensional (3D) images. This article introduces a 3D segmentation framework that combines Swin Transformers and 3D U- Net architectures, leveraging the complementary strengths of these models to improve segmentation accuracy and generalizability for metastatic brain tumors. We train multiple 3D U- Net and Swin U- Net models, selecting the best- performing architectures for segmenting tumor voxels. The outputs of these networks are then combined using various strategies, such as logical operations and stacking the outputs with the original images, to guide the training of a third model. Our method employs an innovative ensemble approach, integrating these outputs into a unified prediction model to enhance performance reliability. Experimental analysis on a newly released metastasis brain tumor dataset, which to the best of our knowledge has been tested for the first time using our models, yielded an impressive accuracy of 73.47%, validating the effectiveness of the proposed architectures.
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