Nuclei Segmentation Assisted by Super-Resolution in Microscopy Image
Authors: 
Peng Liu, Qian Wang, Ruilong GuoAuthors Info & Claims
Peng Liu, Qian Wang, Ruilong GuoAuthors Info & ClaimsPages 650 - 656
Abstract
Automated nuclei segmentation in microscopy images is necessary to detect, quantify, and visualize the nuclei. Nuclei segmentation and counting can be used to provide a reliable reference in the diagnosis and treatment of clinical diseases. Segmentation accuracy is strongly affected by image resolution. To address these limitations, we construct a joint supervised nuclei segmentation network based on U-Net to jointly infer higher resolution images and segment nuclei more accurately, which is a multiple-output network. We build two independent network modules: (1) the segmentation module learns the features within the low-resolution image blocks to label the probabilities of the nuclei at the center of each block, and (2) the super-resolution module learns to map the low-resolution image blocks to the high-resolution image blocks. The joint supervised network can gradually improve the image quality and nuclei segmentation results. The network is a spatially scaled up simulation of the nuclei boundaries recognition task, where the super-resolution module provides higher-scale spatial information for the boundaries segmentation, and the segmentation module directs the super-resolution module to pay more attention to the boundaries region. We have applied our approach to microscopy images to demonstrate the utility of our joint nuclei segmentation and resolution enhancement methods for nuclei segmentation.
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- Nuclei Segmentation Assisted by Super-Resolution in Microscopy Image
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Published In
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 14 June 2024
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AIPR 2023
AIPR 2023: 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 22 - 24, 2023
Xiamen, China
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