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A Weakly Supervised Method With Colorization for Nuclei Segmentation Using Point Annotations
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DOI：10.1109/TIM.2023.3265124.png)
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Nuclei segmentation is an essential step in the automatic analysis of histopathology images. This segmentation task requires much work to manually generate accurate pixel-level annotations for fully supervised training. To overcome such expensive and hard-to-get pixel-level annotations, in this article, we propose a weakly supervised method for nuclei segmentation only using point annotations based on convolutional neural network (CNN). The proposed method effectively combines a weakly supervised segmentation task and an auxiliary colorization task. The dual input with boundaries and color information maximizes the inherent features of the image. Two types of coarse labels generated from point annotations are applied to provide constraint information for the segmentation task. As an auxiliary task, colorization is incorporated to guide the network to extract effective features and improve the segmentation performance of the network. We evaluate our proposed method on two public nuclei segmentation datasets. The experimental results indicate that our method is superior to other state-of-the-art methods.
Keyword:
Annotations
Image segmentation
Image color analysis
Task analysis
Histopathology
Training
Feature extraction
Colorization
deep learning
nuclei segmentation
point annotations
weakly supervised
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
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