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Fine-grained histopathological cell segmentation through residual attention with prior embedding

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Abstract

In the task of histopathological cell segmentation, traditional algorithms struggle with cell edge processing, which leads to the blurring of cell edges. To strengthen the ability to learn the features of cell edges, this paper develops a novel deep neural network for robust and fine-grained cell segmentation. The proposed deep model mines global and local features by multiscale convolution and dilated convolution. Subsequently, the residual attention module is introduced in the third to fifth layers of the encoder; this module assigns a group of weight coefficients to all the deep features to boost the segmentation performance. In addition, to further improve the quality of the features in the decoder, we first introduce the strategy of U-Net for the extraction of prior information, where we filter the fused features and compress the features by using the prior information and the filtered features again to integrate more semantic information into the feature refinement in the decoding process. We tested the model on three public data sets: Multiorgan Nucleus Segmentation (MoNuSeg) (Dice 94.9%), Triple Negative Breast Cancer (TNBC) (Dice 95.4%) and Data Science Bowl (Dice 98.2%). Extensive experiments demonstrate the superior performance of our proposed method in comparison with that of state-of-the-art models; our method can effectively identify cell edges to produce fine-grained segmentation results.

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Tangqi Shi, Chaoqun Li, Dou Xu & Xiayue Fan

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  1. Tangqi Shi
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  2. Chaoqun Li
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  3. Dou Xu
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  4. Xiayue Fan
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Correspondence to Tangqi Shi.

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Shi, T., Li, C., Xu, D. et al. Fine-grained histopathological cell segmentation through residual attention with prior embedding. Multimed Tools Appl 81, 6497–6511 (2022). https://doi.org/10.1007/s11042-021-11835-7

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  • DOI: https://doi.org/10.1007/s11042-021-11835-7

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