A Robust U-Net-Based Approach for Accurate Brain Tumor Segmentation Using Multimodal MRI Data by Mohammad Talal Ghazal

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3D densely connected CNN with multi-scale receptive fields and hybrid loss for brain tumor segmentation by Fatemehzahra Adib, Maryam Amirmazlaghani, Mohammad Rahmati

“…The proposed method is evaluated on the MICCAI BraTS (MICCAI BraTS 2017 (Brain Tumor Segmentation Challenge at the Medical Image Computing and Computer Assisted Intervention Conference 2017) is a publicly available dataset of multimodal MRI scans for brain tumor segmentation) 2017 dataset, achieving Dice similarity coefficients of 0.95, 0.89, and 0.89 for the whole tumor, tumor core, and enhancing tumor regions, respectively. …”
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An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features by Mücahid Barstuğan

“…Brain tumor images obtained through 3D magnetic resonance imaging were used to classify high-grade and low-grade glioma in the BraTS 2017 dataset. From the dataset, texture properties for each of the four phases (i.e., FLAIR, T1, T1c, and T2) were extracted using a 3D gray level co-occurrence matrix. …”
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Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI by Annachiara Cariola, Elena Sibilano, Andrea Guerriero, Vitoantonio Bevilacqua, Antonio Brunetti

“…Two pipelines were developed for segmenting enhanced tumor (ET), tumor core (TC), and whole tumor (WT) in pediatric gliomas from the BraTS-PEDs 2024 dataset. First, a pre-trained SegResNet model was retrained with a transfer learning approach and tested on the pediatric cohort. …”
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Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications by Marjan Vatanpour, Javad Haddadnia, Shahryar Salmani Bajestani

“…Results: The proposed model used for the segmentation of the BraTS2021 dataset showed the average Dice score of 0.86 and the average Hausdorff of 17.94. …”
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Comparative Analysis of Hybrid Attention and Progressive Layering Through a Comprehensive Evaluation of ARU-Net and PLU-Net in Brain Tumour Segmentation by Noman Ahmed Siddiqui, Muhammad Tahir Qadri, Muhammad Ovais Akhter, Tahira Anwar, Fahad Farooq

“…The current study is an in-depth comparative study of two different deep-learning models the Novel Hybrid Channel Attention Regression U-Net (ARU-Net) and Progressive Layered U-Net (PLU-Net) that build on the U-Net architecture as an enhancement to increase the segmentation accuracy of the BraTS 2021 dataset with respect to the T1, T1ce, T2, and FLAIR modalities. …”
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Masked and Noise-Masked Multimodal Brain Tumor Image Segmentation Using SegFormer and Shared Encoder Framework by K. Hemalatha, P. R. Vishnu Vardhan, Alfred Dharmaraj Aravindraj, S. Hari Hara Sudhan

“…Evaluation on Brain Tumor Segmentation (BraTS2020) challenge dataset demonstrates that MNMS outperforms conventional convolutional neural network (CNN) based methods, achieving superior accuracy and Dice Similarity Coefficient (DSC) scores. …”
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