期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 64, 期 12, 页码 2901-2912出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2017.2686418
关键词
Convolutional neural network; instance segmentation; histology image; multichannel; segmentation
资金
- Microsoft Research under the eHealth program
- Beijing National Science Foundation in China [4152033]
- Technology and Innovation Commission of Shenzhen in China [shenfagai 2016-627]
- Beijing Young Talent Project in China
- Fundamental Research Funds for the Central Universities of China under the State Key Laboratory of Software Development Environment in Beihang University in China [SKLSDE-2015ZX-27]
Objective: A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not only need to be segmented from a complex background, they must also be individually identified. Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information-regional, location, and boundary cues-in gland histology images. Our proposed algorithm, a deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirements by altering channels. Results: Compared with methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent instance segmentation methods, we observe state-of-the-art results based on the evaluation metrics. Conclusion: The proposed deep multichannel algorithm is an effective method for gland instance segmentation. Significance: The generalization ability of our model not only enable the algorithm to solve gland instance segmentation problems, but the channel is also alternative that can be replaced for a specific task.
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