Test-time augmentation for deep learning-based cell segmentation on microscopy images.

ABSTRACT: Recent advancements in deep learning have revolutionized the way microscopy images of cells are processed. Deep learning network architectures have a large number of parameters, thus, in order to reach high accuracy, they require a massive amount of annotated data. A common way of improving accuracy builds on the artificial increase of the training set by using different augmentation techniques. A less common way relies on test-time augmentation (TTA) which yields transformed versions of the image for prediction and the results are merged. In this paper we describe how we have incorporated the test-time argumentation prediction method into two major segmentation approaches utilized in the single-cell analysis of microscopy images. These approaches are semantic segmentation based on the U-N

SUBMITTER: Moshkov N 

PROVIDER: S-EPMC7081314 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature