Proposal for Enhancing Usability of ERnet through Advanced Pre-processing
Background:
The ERnet (https://pubmed.ncbi.nlm.nih.gov/36997816/), built upon the robust foundation of the Swin Transformer architecture, represents a cutting-edge solution for the segmentation of the endoplasmic reticulum (ER), excelling particularly with super-resolved ER structures.
Problem Statement:
ERnet achieves high performance on high-quality image inputs, such as SIM images and high SNR widefield or confocal data. However, widefield and confocal data with poor imaging quality such as low SNR remains incompatible with the ERnet processing framework.
Aim:
To address this challenge, our objective is to design and implement a sophisticated pre-processor capable of transforming lower quality images into high-definition equivalents. Leveraging denoising methodologies, such as the diffusion model, will be pivotal in achieving this transformation.
Overall Direction:
By integrating principles of super-resolution from computer vision, our intent is to upgrade wide-field and confocal data inputs, thus broadening the applicability and usability of ERnet. Through this enhancement, we aim to make ERnet a universally compatible tool, ensuring researchers can harness its capabilities irrespective of their data quality.
Validation: against current version (and others such segment anything) and against ground truth
supervisors: Pietro Lio' (pl219@), Edward Ward (ew535@), Meng Lu (ml600@)
