Automatic cell counting from stimulated Raman imaging using deep learning.

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 num

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC8294532 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature