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Updated: Mar 10, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Attention-enhanced SAM with PBFO tuning: advancing glioma MRI segmentation
Salem Alhatamleh1, Hamad Yahia Abu Mhanna2, Mohammad Amin1
1Department of Computer Science, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan.
Introduction:
The segmentation of brain tumor MRI images is one of the most challenging tasks because of the variability and complexity associated with tumor tissues. This study introduces PoSAM-ULTRA, an improved segmentation framework designed to enhance the accuracy and robustness of brain tumor segmentation.
Methods:
PoSAM-ULTRA employs the Polar-Bear Foraging Optimisation (PBFO) algorithm for hyperparameter tuning and utilizes an improved Segment Anything Model as its backbone. The framework is based on a ResNet-34 encoder modified to accept a four-channel input (RGB + prior information). Multi-scale feature extraction is performed via DownBlocks, while discriminative feature learning is enhanced using the Convolutional Block Attention Module (CBAM). Attention Gates are incorporated to ensure effective skip connections, and a multistage decoder is used for robust upsampling and feature integration. The model was evaluated on a dataset from the Integrative Genomic Analysis of Diffuse Lower Grade Gliomas (LGG) and compared with UNet, UNet++, and nnUNet.
Results:
The proposed PoSAM-ULTRA model outperformed the baseline models, achieving superior performance with a Dice score of 91.4%, IoU of 88.9%, Accuracy of 99.8%, Precision of 95.2%, and Recall of 93.3%.
Discussion:
The obtained results demonstrate the robustness and reliability of PoSAM-ULTRA in handling the complexity of brain tumor MRI segmentation, highlighting its effectiveness for challenging medical image segmentation tasks.
