Next Article in Journal
An Integrated Approach to Determining the Capacity of Ecosystems to Supply Ecosystem Services into Life Cycle Assessment for a Carbon Capture System
Previous Article in Journal
Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme
Previous Article in Special Issue
Quantification of Liver Fibrosis—A Comparative Study
Open AccessArticle

Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning

1
Department of Applied Mathematics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
2
Department of Applied Informatics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
3
Department of Mathematical Modelling, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(2), 615; https://doi.org/10.3390/app10020615
Received: 19 December 2019 / Revised: 6 January 2020 / Accepted: 8 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Image Processing Techniques for Biomedical Applications)
(1) Background: The segmentation of cell nuclei is an essential task in a wide range of biomedical studies and clinical practices. The full automation of this process remains a challenge due to intra- and internuclear variations across a wide range of tissue morphologies, differences in staining protocols and imaging procedures. (2) Methods: A deep learning model with metric embeddings such as contrastive loss and triplet loss with semi-hard negative mining is proposed in order to accurately segment cell nuclei in a diverse set of microscopy images. The effectiveness of the proposed model was tested on a large-scale multi-tissue collection of microscopy image sets. (3) Results: The use of deep metric learning increased the overall segmentation prediction by 3.12% in the average value of Dice similarity coefficients as compared to no metric learning. In particular, the largest gain was observed for segmenting cell nuclei in H&E -stained images when deep learning network and triplet loss with semi-hard negative mining were considered for the task. (4) Conclusion: We conclude that deep metric learning gives an additional boost to the overall learning process and consequently improves the segmentation performance. Notably, the improvement ranges approximately between 0.13% and 22.31% for different types of images in the terms of Dice coefficients when compared to no metric deep learning. View Full-Text
Keywords: nuclei detection; image segmentation; deep learning; metric embeddings; digital pathology nuclei detection; image segmentation; deep learning; metric embeddings; digital pathology
Show Figures

Figure 1

MDPI and ACS Style

Iesmantas, T.; Paulauskaite-Taraseviciene, A.; Sutiene, K. Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning. Appl. Sci. 2020, 10, 615.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop