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Abstract #1859

Comparative Analysis of Deep Learning Models for Brain Tumor Segmentation in MRI Scans Using BraTS and Experimental Datasets

Sankar Narayan Misra1,2, Subhanon Bera1,2, Sourav Basak1, Sonal Sarkar1, Archith Rajan3, Suyash Mohan3, Harish Poptani4, Sanjeev Chawla3, and Sourav Bhaduri1,2
1Institute for Advancing Intelligence, TCG Centres for Research & Education in Science & Technology, Kolkata, India, 2Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India, 3Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 4Centre for Preclinical Imaging, University of Liverpool, Liverpool, United Kingdom

Synopsis

Keywords: Analysis/Processing, Tumors

Motivation: Precise brain tumor segmentation from MRI is essential for accurate tumor volume calculation and assessing treatment effectiveness, but traditional methods are labor-intensive and inaccurate, driving exploration of deep learning approaches.

Goal(s): To evaluate the performance of six UNet variants on BraTS and private-dataset, assessing adaptability and accuracy to identify the best model for further research.

Approach: Comparative analysis of nnU-Net, U-Net++, ELU-Net, Attention-UNet, and UNETR, using Dice and Jaccard metrics. Transfer-learning was applied to mitigate dataset-specific performance drops.

Results: nnU-Net demonstrated the best performance on both datasets, reaching an 89% Dice score on BraTS and 84.5% on the private-dataset after transfer-learning.

Impact: It highlights potential of deep-learning models, particularly nnU-Net to improve accuracy and efficiency in brain-tumor segmentation, reducing reliance on labor-intensive methods like RANO and iRANO. Addressing dataset-specific limitations through transfer-learning, the findings aids in consistent tumor-volume assessment, enhancing treatment monitoring.

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