A Deep Learning Pipeline for Nucleus Segmentation

BioRxiv : the Preprint Server for Biology
G. ZakiGianluca Pegoraro

Abstract

Deep learning is rapidly becoming the technique of choice for automated segmentation of nuclei in biological image analysis workflows. In order to evaluate the feasibility of training nuclear segmentation models on small, custom annotated image datasets that have been augmented, we have designed a computational pipeline to systematically compare different nuclear segmentation model architectures and model training strategies. Using this approach, we demonstrate that transfer learning and tuning of training parameters, such as the composition, size and pre-processing of the training image dataset, can lead to robust nuclear segmentation models, which match, and often exceed, the performance of existing, off-the-shelf deep learning models pre-trained on large image datasets. We envision a practical scenario where deep learning nuclear segmentation models trained in this way can be shared across a laboratory, facility, or institution, and continuously improved by training them on progressively larger and varied image datasets. Our work provides computational tools and a practical framework for deep learning-based biological image segmentation using small annotated image datasets.

Related Concepts

Transcriptional Regulation
Research
Genome
Transcription, Genetic
Genomic Stability
Promoter
Genomics
Transcription Factor Binding
Histone Modification
Binding (Molecular Function)

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

CREs: Gene & Cell Therapy

Gene and cell therapy advances have shown promising outcomes for several diseases. The role of cis-regulatory elements (CREs) is crucial in the design of gene therapy vectors. Here is the latest research on CREs in gene and cell therapy.

© 2020 Meta ULC. All rights reserved