DeepCeNS: An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images

Nabeel Khalid, Mohsin Munir, Christoffer Edlund, Timothy R Jackson, Johan Tryggy, Rickard Sjögren, Andreas Dengel, Sheraz Ahmed

In: 2021 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN-2021) July 18-20 Glasgow United Kingdom IJCNN 2021.


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 RCNN 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.


DeepCeNS.pdf (pdf, 4 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence