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Learning Pseudo Scale Instance Maps for Cell Localization
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  • Chengyang Zhang ,
  • Jie Chen ,
  • Bo Li ,
  • Min Feng ,
  • Yongquan Yang ,
  • Qikui Zhu ,
  • Hong Bu
Chengyang Zhang
the Department of Pathology and Institute of Clinical Pathology, the Department of Pathology and Institute of Clinical Pathology

Corresponding Author:[email protected]

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Yongquan Yang
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Qikui Zhu
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In the field of medical image analysis, especially in the study of pathological morphology images of tumor tissues, accurately locating cells in images is a crucial task. However, this task faces multiple challenges, such as differences in cell staining intensity and cell morphology, and variance in cell shape and scale caused by manual stretching. Currently, the solution is to use fixed-scale density maps to supervise the model’s training, which makes it difficult for the model to handle cells of different scales and cell localization scenarios under different magnifications. To address this problem, this paper proposes a new cell location map called Pseudo Scale Instance (PSI) map, which has instance-level scale information. Additionally, to address the problem of significant differences between complex cell shapes and masks in PSI maps, this paper introduces a gradient alignment module based on Difference Deformable Convolution (DDC). The module uses gradient information to calculate the offsets in the deformable convolution, making the model’s output closer to the ground truth. Experiments show that the PSI map proposed in this paper can effectively utilize scale information to improve the baseline performance of cell localization and counting. Moreover, the DDC module can effectively bridge the gap between cell images and ground truth, thereby alleviating the problem of diverse cell shapes. Compared with existing methods, the proposed method has significantly improved the localization performance on two public datasets, setting a new benchmark for cell localization tasks.
Jan 2024Published in IEEE Journal of Biomedical and Health Informatics volume 28 issue 1 on pages 355-366. 10.1109/JBHI.2023.3329542