Automated Segmentation of Brain Gliomas in Multimodal MRI Data

Abstract

Brain gliomas, common in adults, pose significant diagnostic challenges. Accurate segmentation from multimodal magnetic resonance imaging (MRI) scans is critical for effective treatment planning. Traditional manual segmentation methods, labor-intensive and error-prone, often lead to inconsistent diagnoses. To overcome these limitations, our study presents a sophisticated framework for the automated segmentation of brain gliomas from multimodal MRI images. This framework consists of three integral components: a 3D UNet, a classifier, and a Classifier Weight Transformer (CWT). The 3D UNet, acting as both an encoder and decoder, is instrumental in extracting comprehensive features from MRI scans. The classifier, employing a streamlined 1 × 1 convolutional architecture, performs detailed pixel-wise classification. The CWT integrates self-attention mechanisms through three linear layers, a multihead attention module, and layer normalization, dynamically refining the classifier's parameters based on the features extracted by the 3D UNet, thereby improving segmentation accuracy. Our model underwent a two-stage training process for maximum efficiency: in the first stage, supervised learning was used to pre-train the encoder and decoder, focusing on robust feature representation. In the second stage, meta-training was applied to the classifier, with the encoder and decoder remaining unchanged, ensuring precise fine-tuning based on the initially developed features. Extensive evaluation of datasets such as BraTS2019, BraTS2020, BraTS2021, and a specialized private dataset (ZZU) underscored the robustness and clinical potential of our framework, highlighting its superiority and competitive advantage over several state-of-the-art approaches across various segmentation metrics in training and validation sets.