Multiresolution U-Net
A multiresolution network based on the U-Net architecture handles the low number of annotated images.
Abstract
The quantitative and qualitative ascertainment of cell culture is integral to the robust determination of the cell structure analysis. Microscopy cell analysis and the epithet structures of cells in cell cultures are momentous in the fields of the biological research process. In this paper, we addressed the problem of phase-contrast microscopy under cell segmentation application. In our proposed method, we utilized the state-of-the-art deep learning models trained on our proposed dataset. Due to the low number of annotated images, we propose a multiresolution network which is based on the U-Net architecture. Moreover, we applied multi-combination augmentation to our dataset which has increased the performance of segmentation accuracy significantly. Experimental results suggest that the proposed model provides superior performance in comparison to traditional state-of-the-art segmentation algorithms.
Framework
Multi-combination augmentation
Augmentation of the proposed dataset increases segmentation accuracy significantly.
Superior performance
Experiments show the model outperforms traditional state-of-the-art segmentation algorithms.
Resources
- Publisher
- IEEE Xplore (DOI)
BibTeX
@inproceedings{ayanzadeh2019cell,
title = {Cell Segmentation of 2D Phase-Contrast Microscopy Images with Deep Learning Method},
author = {Ayanzadeh, Aydin and Ya{\u{g}}ar, H{\"u}seyin Onur and {\"O}zuysal, {\"O}zden Yal{\c{c}}in and Okvur, Devrim Pesen and T{\"o}reyin, Beh{\c{c}}et U{\u{g}}ur and {\"U}nay, Devrim and {\"O}nal, Sevgi},
booktitle = {2019 Medical Technologies Congress (TIPTEKNO)},
pages = {1--4},
year = {2019},
organization = {IEEE},
doi = {10.1109/TIPTEKNO.2019.8894978}
}