| Title |
Enhancing multi-tissue and multi-scale cell nuclei segmentation with deep metric learning / |
| Authors |
Iesmantas, Tomas ; Paulauskaite-Taraseviciene, Agne ; Sutiene, Kristina |
| DOI |
10.3390/app10020615 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2020, vol. 10, iss. 2, art. no. 615, p. 1-15.. ISSN 2076-3417 |
| Keywords [eng] |
nuclei detection ; image segmentation ; deep learning ; metric embeddings ; digital pathology |
| Abstract [eng] |
(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. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2020 |
| CC license |
|
