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动态感受野的图像分割神经网络模型

Dynamically Receptive Field Networks for Image Segmentation

  • 摘要: 针对基于U-net的分割方法在医学图像分割领域存在的下采样阶段信息丢失和因固定尺寸卷积核带来的局部多尺度语义信息提取不足的问题, 提出一种动态感受野的神经网络模型. 首先通过构建特征递进级联模块获取编码器多尺度局部语义特征并将其赋值给解码器, 提升模型解码阶段图像语义信息修复的效果; 然后设计局部视野偏移模块来增强固定尺寸卷积核提取视野内上下文语义信息的能力. 在ISIC2018和BUSI癌症图像分割数据集上, 本文所提方法IoU指标达到了83.92±0.26和70.45±1.70, Dice系数达到了91.09±0.23和83.39±1.15, 比现有医学图像分割方法更优.

     

    Abstract: To address the issues of information loss during down-sampling and the insufficient extraction of the multi-scale local semantic information caused by the fixed-size convolutional kernels when applying a U-Net-based method to realize medical image segmentation, a dynamically receptive field network (DRFN) is proposed. Firstly, the network enhances the reducibility of the semantic information during decoding process by introducing the feature progressively cascaded integration (FPCI) module to extract multi-scale local semantic features from the encoder and assigns these features to the decoder. On this basis, the local vision adjustment (LVA) module is designed to strengthen the ability of the fixed-size convolution kernel to obtain contextual semantic information in a large receptive field. The experimental results on the ISIC2018 and BUSI cancer image segmentation datasets show that the IoU score of the proposed method reaches 83.92±0.26 and 70.45±1.70 while the Dice score reaches 91.09±0.23 and 83.39±1.15, which outperforms the state-of-the-art medical image segmentation methods.

     

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