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Zhifei Yang

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Proceedings Article | 28 August 2023 Paper

Enhanced U-Net++ for brain tumor segmentation based on data enhancement

Zhifei Yang, Shouqiang Jia, Yanfeng Lu

Proceedings Volume 12724, 127241U (2023) https://doi.org/10.1117/12.2687856

KEYWORDS: Image segmentation, Data modeling, Tumors, Education and training, Brain, Medical imaging, Image processing algorithms and systems, Image fusion, Neuroimaging, Evolutionary algorithms

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Accurate identification and removal of the tumor is critical for effective treatment of brain tumors. Manual identification of the tumor location from medical images can be challenging. Hence, modern medicine relies increasingly on artificial intelligence models to assist with segmentation. Despite this, currently available medical image segmentation models still do not produce satisfactory results in segmenting lesions like tumors. Many times, medical data is not sufficient for models to learn effectively, making it difficult to accurately segment the target. Therefore, we propose a new model, E-U-Net++, it combines data augmentation methods with existing U-Net++ series models enabling better segmentation results even in situations where medical data is generally scarce. In the training phase, before inputting the original training data to the U-Net++ model, we incorporated various data augmentation techniques, including random flipping, cropping, and the addition of noise. During the testing phase, the preprocessed data was subjected to center cropping and then provided to trained U-Net++ weight for processing. Experimental tests comparing the testing consequences of E-U-Net++ with the U-Net and U-Net++ algorithm revealed that the improved E-U-Net++ algorithm outperformed these models in brain tumor segmentation. Enhancing the original segmentation model through data augmentation methods holds great potential in improving brain tumor segmentation performance.

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