Automatic Improvement of Deep Learning Based Cell Segmentation in Time-Lapse Microscopy by Neural Architecture Search.

ABSTRACT: Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent years, numerous deep learning based methods have been proposed to tackle this task and obtained promising results. However, designing a network with excellent performance requires professional knowledge and expertise and is very time-consuming and labor-intensive. Recently emerged neural architecture search (NAS) methods hold great promise in eliminating these disadvantages, because they can automatically search an optimal network for the task. We propose a novel NAS based solution for deep-learning based cell segmentation in time-lapse microscopy images. Dif

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC8665766 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature