Double U-Net for Super-Resolution and Segmentation of Live Cell Images
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 5 Dec 2022.
Builds on
8 of 20 references.
References · 8
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
- 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
- 2019Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practicecited by 71
- 2018Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Networkcited by 64
- 2016RAISR: Rapid and Accurate Image Super Resolutioncited by 27
- 2017Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networkscited by 21
- 2017Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Losscited by 3
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Citing papers per year: 2023: 1.
1 paper.