Article
Automatic cell counting from stimulated Raman imaging using deep learning
PLOS One
(2021)
Abstract
In this paper, we propose an automatic cell counting framework for stimulated Raman scattering
(SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis,
and surgery planning processes. SRS microscopy has promoted tumor diagnosis and
surgery by mapping lipids and proteins from fresh specimens and conducting a fast disclose
of fundamental diagnostic hallmarks of tumors with a high resolution. However, cell counting
from label-free SRS images has been challenging due to the limited contrast of cells and tissue,
along with the heterogeneity of tissue morphology and biochemical compositions. To
this end, a deep learning-based cell counting scheme is proposed by modifying and applying
U-Net, an effective medical image semantic segmentation model that uses a small number
of training samples. The distance transform and watershed segmentation algorithms are
also implemented to yield the cell instance segmentation and cell counting results. By performing
cell counting on SRS images of real human brain tumor specimens, promising cell
counting results are obtained with > 98% of area under the curve (AUC) and R = 0.97 in
terms of cell counting correlation between SRS and histological images with hematoxylin
and eosin (H&E) staining. The proposed cell counting scheme illustrates the possibility and
potential of performing cell counting automatically in near real time and encourages the
study of applying deep learning techniques in biomedical and pathological image analyses.
Keywords
- Pathological cell counting,
- Machine Learning image data processing
Disciplines
Publication Date
2021
DOI
https://doi.org/ 10.1371/journal.pone.0254586
Citation Information
Qianqian Zhang, Max K. Yun, Hao Wang, Sang Won Yoon, et al.. "Automatic cell counting from stimulated Raman imaging using deep learning" PLOS One (2021) Available at: http://works.bepress.com/max-yun/4/