Brain Tumor Auto Segmentation On 3D MRI Using Deep Neural Network

Melda Agarina; Muh Royan Fauzi Maulana; Sutedi Sutedi; Arman Suryadi Karim

doi:10.52436/1.jutif.2025.6.5.5106

Authors

Melda Agarina Department of Computer Science, Informatics and Business Institute Darmajaya, Bandar Lampung, Lampung, Indonesia
Muh Royan Fauzi Maulana Department of Computer Science, Informatics and Business Institute Darmajaya, Bandar Lampung, Lampung, Indonesia
Sutedi Department of Computer Science, Informatics and Business Institute Darmajaya, Bandar Lampung, Lampung, Indonesia
Arman Suryadi Karim Department of Computer Science, Informatics and Business Institute Darmajaya, Bandar Lampung, Lampung, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.5106

Keywords:

Brain Tumor, CNN, DNN, Segmentation, 3D UNet

Abstract

Accurate and automated segmentation of brain tumours from Magnetic Resonance Imaging (MRI) is crucial for clinical diagnosis and treatment planning, yet it remains a significant challenge due to tumour heterogeneity and data imbalance. This research investigation examines the effectiveness of a 3D UNet architecture for the segmentation of brain tumours utilizing MRI imaging modalities. The research employs the BRATS 2021 dataset, which consists of 675 MRI datasets across four distinct imaging modalities (FLAIR, T1-Weighted, T1-Contrast, and T2-Weighted) and encompasses four distinct segmentation label classes. The employed model integrated soft dice loss and dice coefficient as its loss functions, with the objective of achieving convergence despite the presence of imbalanced data. While constraints related to resources limited the training process, the model yielded promising outcomes, exhibiting high accuracy (99.43%) and specificity (99.5%), The model aids medical professionals in understanding tumor growth and enhances treatment planning via segmentation predictions in surgery. Nevertheless, the sensitivity, particularly concerning non-enhancing tumour classes, persists as a significant challenge, underscoring the necessity for future research to concentrate on data-centric methodologies and enhanced pre-processing techniques to improve model efficacy in critical medical applications such as the segmentation of brain tumours.

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