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arXiv · 2405.04064

MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation

Yanli Yuan·Bingbing Wang·Chuan Zhang·Jingyi Xu·Ximeng Liu·Liehuang Zhu
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Abstract

Segmentation of organs of interest in medical CT images is beneficial for diagnosis of diseases. Though recent methods based on Fully Convolutional Neural Networks (F-CNNs) have shown success in many segmentation tasks, fusing features from images with different scales is still a challenge: (1) Due to the lack of spatial awareness, F-CNNs share the same weights at different spatial locations. (2) F-CNNs can only obtain surrounding information through local receptive fields. To address the above challenge, we propose a new segmentation framework based on attention mechanisms, named MFA-Net (Multi-Scale Feature Fusion Attention Network). The proposed framework can learn more meaningful feature maps among multiple scales and result in more accurate automatic segmentation. We compare our proposed MFA-Net with SOTA methods on two 2D liver CT datasets. The experimental results show that our MFA-Net produces more precise segmentation on images with different scales.

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Yanli Yuan, Bingbing Wang, Chuan Zhang, Jingyi Xu, Ximeng Liu, Liehuang Zhu. 2024-05-07. MFA-Net: Multi-Scale feature fusion attention network for liver tumor segmentation. https://arxiv.org/abs/2405.04064

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