Towards pixel-to-pixel deep nucleus detection in microscopy images.

ABSTRACT:

Background

Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed.

Results

We analyze the applicability of deep models specifically for nucleus detection across a wide vari

SUBMITTER: Xing F 

PROVIDER: S-EPMC6744696 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature