The Development of the RSU U2 Net+ Architecture for Brain Tumor Segmentation in 3D Images
DOI:
https://doi.org/10.24203/0ey9jz30Keywords:
Computer Vision, Deep Learning, RSU U^2 Net, Brain TumorAbstract
Segmenting brain tumors in medical images plays a crucial role in diagnosis and monitoring of medical conditions. However, the segmentation process is still performed manually, consuming time and exhibiting variability among assessors. This research aims to develop the RSU U2-Net+ architecture for brain tumor multilabel segmentation in 3D images. The RSU U2-Net+ architecture consists of 9 interconnected blocks, employing broader connectivity in each block. The architecture is reinforced with the use of Residual U-blocks (RSU) to enhance image understanding across various scales without significantly increasing computational load. Testing on data reveals that the RSU U2-Net+ architecture performs well, as indicated by a dice coefficient score of 0.779, IoU of 0.6439, recall of 0.7541, and specificity of 0.9911. Evaluation is also conducted for each tumor label. Recall and specificity for edema are 0.8690 and 0.9851, for enhancing tumor are 0.7991 and 0.9956, and for non-enhancing tumor are 0.5942 and 0.9927. This research makes a significant contribution to the development of advanced medical image analysis technology. The achieved results have tangible benefits for medical practitioners and patients, with the potential to enhance the speed and consistency of brain tumor segmentation in 3D medical images.
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