多尺度扩展融合网络(MSDFN)用于仪器自动分割

W. Devi, S. Roy, Khelchandra Thongam
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引用次数: 4

摘要

随着语义分割领域的发展,像U-Net这样的编码器-解码器方法被广泛用于解决生物医学图像分割任务。为了改进现有的U-Net,我们提出了一种新的多尺度扩展融合网络(MSDFNet)架构。在这项工作中,我们使用预训练的ResNet50作为编码器,它已经学习了解码器可以使用的特征来生成二进制掩码。此外,我们使用跳过连接来直接促进从编码器到解码器的特征转移。由于网络的深度,其中一些特征丢失了。该解码器由一个多尺度扩展融合块组成,作为解码器的主要组成部分,我们对多尺度特征进行融合,然后对其进行扩展卷积。我们在Ksavir-Instrument数据集上训练了U-Net和提出的体系结构,其中提出的体系结构在F1得分上提高了3.701%,在Jaccard得分上提高了4.376%。这些结果表明该模型比现有的U-Net模型有所改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Scale Dilated Fusion Network (MSDFN) for Automatic Instrument Segmentation
With the recent advancements in the field of semantic segmentation, an encoderdecoder approach like U-Net are most widely used to solve biomedical image segmentation tasks. To improve upon the existing U-Net, we proposed a novel architecture called Multi-Scale Dilated Fusion Network (MSDFNet). In this work, we have used the pre-trained ResNet50 as the encoder, which had already learned features that can be used by the decoder to generate the binary mask. In addition, we used skip-connections to directly facilitate the transfer of features from the encoder to the decoder. Some of these features are lost due to the depth of the network. The decoder consists of a Multi-Scale Dilated Fusion block, as the main components of the decoder, where we fused the multiscale features and then applied some dilated convolution upon them. We have trained both the U-Net and the proposed architecture on the Ksavir-Instrument dataset, where the proposed architecture has a 3.701 % gain in the F1 score and 4.376 % in the Jaccard. These results show the improvement over the existing U-Net model.
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