3D U-Net with trans-coder for brain tumor segmentation

Tingting Zhang; Dan Xu; Kangjian He; Hao Zhang; Yuting Fu

doi:10.1117/12.2623549

16 February 2022 3D U-Net with trans-coder for brain tumor segmentation

Tingting Zhang, Dan Xu, Kangjian He, Hao Zhang, Yuting Fu

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831Q (2022) https://doi.org/10.1117/12.2623549
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

3D Medical image segmentation is a basic and key task in computer-aided diagnosis, and glioma has a high degree of non-uniformity and irregular shape in multimodal MRI images. Therefore, accurate and reliable segmentation of brain tumors is still a challenging work in medical image analysis. U-Net has become the de facto standard in various medical image segmentation tasks and has achieved great success. However, due to the inherent locality of convolution operations, U-Net is generally limited in terms of explicitly modeling long-term dependencies. Although this problem is solved in Transformer, it has extreme complexity in terms of calculation and space when processing high-resolution 3D feature maps. In this paper, we propose the Trans-coder, which is embedded in the end of the U-Net encoder to improve the segmentation performance while reducing the amount of calculation. The Trans-coder takes the feature map from U-Net as the input sequence, and extracts the relative position information of the feature map, so as to get more detailed information of the image, and input it into the decoder to obtain good segmentation performance. At the same time, a variational autoencoder is used for regularization to prevent over-fitting problems. our method achieves superior performances to certain competing methods on the Multimodal Brain Tumor Segmentation Challenge (Brats) dataset.

Citation Download Citation

Tingting Zhang, Dan Xu, Kangjian He, Hao Zhang, and Yuting Fu "3D U-Net with trans-coder for brain tumor segmentation", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831Q (16 February 2022); https://doi.org/10.1117/12.2623549

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KEYWORDS

Image segmentation

Tumors

Medical imaging

3D image processing

Brain

Transformers

Convolution

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