HPNet: hybrid pyramid network for cardiac segmentation

Xin Lu; Zhanfang Zhao; Pengpeng Liu

doi:10.1117/12.3051420

18 November 2024 HPNet: hybrid pyramid network for cardiac segmentation

Xin Lu, Zhanfang Zhao, Pengpeng Liu

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032J (2024) https://doi.org/10.1117/12.3051420
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Segmentation of medical images is pivotal in the progression of proficient healthcare, especially the segmentation of heart images, which is one of its essential research contents. The U-Net architecture has demonstrated remarkable success across diverse segmentation of medical images tasks. Due to the inherent locality of convolution operations, the classical U-Net architecture exhibits challenges in image segmentation, manifesting as reduced resolution and information loss. Inspired by Transformers, the Vision Transformer model applied to classification tasks has good results. In this paper, a refined medical image segmentation model, HPNet, is proposed, and an encoder module that fuses convolutional network and Transformer structure is designed. The modules are paralleled to extract image features. Can grasp the global and local information of the image wellhouse modules are interconnected in parallel to extract image features, thereby enabling effective capture of both global and local information within the image. In the HPNet model, the encoder structure part of the transformer module and the CNN module are connected in parallel to process the input image. After upsampling the encoded features, the decoder merges them with the high-resolution feature map from the encoder section (the output of CNN and ViT). Secondly, this paper uses pyramid pooling operation in the Transformer module to reduce the complexity of the process and uses Shift MLP to improve the segmentation accuracy. The experimental findings indicate that through reduced computational complexity, the proposed HPNet model can improve the performance of the ACDC heart dataset by 1%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xin Lu, Zhanfang Zhao, and Pengpeng Liu "HPNet: hybrid pyramid network for cardiac segmentation", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032J (18 November 2024); https://doi.org/10.1117/12.3051420

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KEYWORDS

Image segmentation

Transformers

Visual process modeling

Data modeling

Medical imaging

Image processing

Performance modeling

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