Double U-Net for Super-Resolution and Segmentation of Live Cell Images
This addresses a resource constraint issue in biomedical imaging, enabling segmentation with limited access to high-performance microscopes, but it is incremental as it combines existing techniques.
The paper tackles the problem of segmenting live cell images from low-resolution inputs by introducing a super-resolution pre-processing step, achieving improved segmentation accuracy.
Accurate segmentation of live cell images has broad applications in clinical and research contexts. Deep learning methods have been able to perform cell segmentations with high accuracy; however developing machine learning models to do this requires access to high fidelity images of live cells. This is often not available due to resource constraints like limited accessibility to high performance microscopes or due to the nature of the studied organisms. Segmentation on low resolution images of live cells is a difficult task. This paper proposes a method to perform live cell segmentation with low resolution images by performing super-resolution as a pre-processing step in the segmentation pipeline.