ADT-UNet: An Innovative Algorithm for Glioma Segmentation in MR Images

Abstract

The precise delineation of glioma tumors is of paramount importance for surgical and radiotherapy planning. Presently, the primary drawbacks associated with the manual segmentation approach are its laboriousness and inefficiency. In order to tackle these challenges, a deep learning-based automatic segmentation technique was introduced to enhance the efficiency of the segmentation process. We proposed ADT-UNet, an innovative algorithm for segmenting glioma tumors in MR images. ADT-UNet leveraged attention-dense blocks and Transformer as its foundational elements. It extended the U-Net framework by incorporating the dense connection structure and attention mechanism. Additionally, a Transformer structure was introduced at the end of the encoder. Furthermore, a novel attention-guided multi-scale feature fusion module was integrated into the decoder. To enhance network stability during training, a loss function was devised that combines Dice loss and binary cross-entropy loss, effectively guiding the network optimization process. On the test set, the DSC was 0.933, the IOU was 0.878, the PPV was 0.942, and the Sen was 0.938. Ablation experiments conclusively demonstrated that the inclusion of all the three proposed modules led to enhanced segmentation accuracy within the model. The most favorable outcomes were observed when all the three modules were employed simultaneously. The proposed methodology exhibited substantial competitiveness across various evaluation indices, with the three additional modules synergistically complementing each other to collectively enhance the segmentation accuracy of the model. Consequently, it is anticipated that this method will serve as a robust tool for assisting clinicians in auxiliary diagnosis and contribute to the advancement of medical intelligence technology.