What type of study is this?

This is a Quantitative Study study.

October 8, 2025

Fully Automated Glioblastoma Segmentation and Classification in Multispectral Magnetic Resonance Images Based on Level Set and Deep Neural Network

Key Points

The proposed method achieves a Dice coefficient of 0.8915, highlighting its effectiveness in glioblastoma segmentation.
Segmentation and classification were evaluated using the BraTS 2020 dataset, showing promising mean values across various performance metrics.
By integrating a level set method with a deep neural network, this framework enhances both the accuracy and efficiency of tumor analysis.
Future improvements will focus on expanding to multi-label segmentation for different tumor sub-regions using larger datasets.

Abstract

ABSTRACT Automatic segmentation and classification methods of glioblastomas in magnetic resonance imaging (MRI) scans are essential to overcome the limitations of error‐prone manual methods, especially given the intrinsic challenges such as intensity nonuniformity, diverse anatomical brain alterations, and significant variations in tumor shape, size, and location. These complexities pose major hurdles for radiologists in diagnosis and surgical planning, underscoring the critical significance of robust automated solutions. This study presents a novel fully automated approach for segmentation and classification of glioblastoma brain tumors using multi‐spectral MRI data. Our proposed framework innovatively integrates two key steps. In the first step, a new level set method is presented for segmentation, which is uniquely enhanced by super‐pixel fuzzy entropy‐based clustering—a technique designed to effectively handle image inhomogeneities and noise—density peak clustering, and a lattice Boltzmann solver for efficient contour evolution. In the second step, a VGG‐16 deep neural network is employed for precise classification. To assess the capability of the proposed method in both segmentation and classification tasks, real T2‐weighted and fluid‐attenuated inversion recovery magnetic resonance images of glioblastomas from the BraTS 2020 dataset are used simultaneously in a multi‐spectral manner. Our segmentation results, evaluated by measuring the Dice coefficient, Jaccard index, sensitivity, specificity, and running time. The mean values (Mean ± Standard deviation) of these metrics are 0.8915 ± 0.0293, 0.8055 ± 0.0478, 0.9535 ± 0.0644, 0.9910 ± 0.0364, 2.2909 ± 0.2597, respectively. Additionally, the average values of accuracy, precision, recall, and F1‐score across the fivefold cross‐validation of the classification method are 0.9149, 0.9532, 0.9160, and 0.9349, respectively. According to the experiments, our proposed fully automated framework not only achieves superior performance in simultaneous segmentation and classification compared to other state‐of‐the‐art segmentation methods but also offers a robust and efficient solution for clinical applications. While this study demonstrates strong potential, future work will focus on extending the framework for multi‐label segmentation of different tumor sub‐regions and validating its efficacy on even larger and more diverse clinical datasets.

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