4D flow MRI study: a label fusion pipeline based on fully convolutional network

Shuqi Guo; Yu Ma; Xue Li; Pengzhi Wang

doi:10.1117/12.2674727

2 May 2023 4D flow MRI study: a label fusion pipeline based on fully convolutional network

Shuqi Guo, Yu Ma, Xue Li, Pengzhi Wang

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126420F (2023) https://doi.org/10.1117/12.2674727
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

In order to solve the problem of time-consuming multi-graph segmentation and fuzzy boundary segmentation, a full-convolution multi-graph segmentation algorithm based on region of interest location and contour perception was proposed. In this model, the ROI positioning module is serial-ized with the fusion network. The ROI positioning module locates the segmented area, reduces the parameters of the subsequent fusion module and the network load, and improves the training speed of the network model. The multi-sensory domain fusion strategy and dense connection are intro-duced to obtain the global and local context information of the image and enhance the detail rich-ness of the image. Then, the DICE loss function based on contour perception was used to constrain the sensitivity of the network to the image segmentation boundary and enhance the segmentation efficiency. Two public brain data sets were used to evaluate the performance of the proposed algorithm by segmenting the hippocampus. The experimental results show that the time cost and seg-mentation accuracy of the proposed algorithm are improved, and the average DSC, HD95 and JSC of the proposed algorithm reach 96.73%, 2.110 and 93.7% respectively.

Citation Download Citation

Shuqi Guo, Yu Ma, Xue Li, and Pengzhi Wang "4D flow MRI study: a label fusion pipeline based on fully convolutional network", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126420F (2 May 2023); https://doi.org/10.1117/12.2674727

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KEYWORDS

Image segmentation

Image fusion

Brain

Magnetic resonance imaging

Image processing algorithms and systems

3D image processing

Neuroimaging

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