Paper
12 October 2022 FSC-UNet: a lightweight medical image segmentation algorithm fused with skip connections
Yixin Chen, Jianjun Zhang, Xulin Zong, ZhiPeng Zhao, Hanqing Liu, Ruichun Tang, Peishun Liu, Jinyu Wang
Author Affiliations +
Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421E (2022) https://doi.org/10.1117/12.2644360
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China
Abstract
In order to study the effect of skip connections to segmentation performance in encoder and decoder networks, in this paper, we improve the skip connections of U-Net model and adopt the method of sub-module fusion connection. We fuse the high and low layers of the encoder by multi-head attention. Fusion is performed separately, and the fusion result is connected to the decoder. Considering that different input images have different effects to model training due to factors such as noise, we set the threshold by calculating the Euclidean distance between the image and the mask during training, so that different images use different skip connection methods. Experiments on Cell nuclei, Synapse, Heart, Chaos datasets show that FSC-UNet algorithm this paper proposed has better results than existing algorithms.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yixin Chen, Jianjun Zhang, Xulin Zong, ZhiPeng Zhao, Hanqing Liu, Ruichun Tang, Peishun Liu, and Jinyu Wang "FSC-UNet: a lightweight medical image segmentation algorithm fused with skip connections", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421E (12 October 2022); https://doi.org/10.1117/12.2644360
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KEYWORDS
Computer programming

Image segmentation

Image fusion

Medical imaging

Convolution

Data modeling

Heart

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