Optimizing deep learning-based segmentation of densely packed cells using cell surface markers.

ABSTRACT:

Background

Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge.

Methods

We first assess the performance of 18 deep learning-based cell segmentation models, either pre-trained or trained by us using two public image sets, on a set of immunofluorescence images stained with immune cell surface markers in skin tissue obtained during human herpes simplex virus (HSV) infection. We then further train eight of these models using up to 10,000+ training instances from the current image set. Finally, we seek to improve performance by tuning parameters of the most successful method from the previous step.

Re

SUBMITTER: Han S 

PROVIDER: S-EPMC11094866 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature