Papers › DeepCeNS: An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images
DeepCeNS: An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images
Nabeel Khalid, Mohsin Munir, Christoffer Edlund, Timothy R Jackson, Johan Trygg, Rickard Sjögren, Andreas Dengel, Sheraz Ahmed
With the evolution of deep learning in the past decade, more biomedical related problems that seemed strenuous, are now feasible. The introduction of U-net and Mask R-CNN architectures has paved a way for many object detection and segmentation tasks in numerous applications ranging from security to biomedical applications. In the cell biology domain, light microscopy imaging provides a cheap and accessible source of raw data to study biological phenomena. By leveraging such data and deep learning techniques, human diseases can be easily diagnosed and the process of treatment development can be greatly expedited. In microscopic imaging, accurate segmentation of individual cells is a crucial step to allow better insight into cellular heterogeneity. To address the aforementioned challenges, DeepCeNS is proposed in this paper to detect and segment cells and nucleus in microscopic images. We have used EVICAN2 dataset which contains microscopic images from a variety of microscopes having numerous cell cultures, to evaluate the proposed pipeline. DeepCeNS outperforms EVICAN-MRCNN by a significant margin on the EVICAN2 dataset.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cell Segmentation | EVICAN | DeepCeNS | mask AP | 52.56 | #1 of 2 | Archive leaderboard | report |
| Cell Segmentation | EVICAN | DeepCeNS | mask AP50 | 83.40 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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