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This research introduces an advanced approach to automate the segmentation and quantification of nuclei in fluorescent images through deep learning techniques. Overcoming inherent challenges such as variations in pixel intensities, noisy boundaries, and overlapping edges, our devised pipeline integrates the U-Net architecture with state-of-the-art CNN models, such as EfficientNet. This fusion maintains the efficiency of U-Net while harnessing the superior capabilities of EfficientNet. Crucially, we exclusively utilize high-quality confocal images generated in-house for model training, purposefully avoiding the pitfalls associated with publicly available synthetic data of lower quality. Our training dataset encompasses over 3000 nuclei boundaries, which are meticulously annotated manually to ensure precision and accuracy in the learning process. Additionally, post-processing is implemented to refine segmentation results, providing morphological quantification for each segmented nucleus. Through comprehensive evaluation, our model achieves notable performance metrics, attaining an F1-score of 87% and an Intersection over Union (IoU) value of 80%. Furthermore, its robustness is demonstrated across diverse datasets sourced from various origins, indicative of its broad applicability in automating nucleus extraction and quantification from fluorescent images. This innovative methodology holds significant promise for advancing research efforts across multiple domains by facilitating a deeper understanding of underlying biological processes through automated analysis of fluorescent imagery.
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Semi Supervised Segmentation And Graph-Based Tracking Of 3d Nuclei In Time-Lapse Microscopy
We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an oversegmented image. This is motivated by the observation that current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. Towards this, a 3D U-Net is trained to get the centroid of the nuclei and integrated with a simple linear iterative clustering (SLIC) supervoxel algorithm that provides better adherence to cluster boundaries. To track these segmented nuclei, our algorithm utilizes the relative nuclei location depicting the processes of nuclei division and apoptosis. The proposed algorithmic pipeline achieves better segmentation performance compared to the state-of-the-art method in Cell Tracking Challenge (CTC) 2019 and comparable performance to state-of-the-art methods in IEEE ISBI CTC2020 while utilizing very few pixel-wise annotated data. Detailed experimental results are provided, and the source code is available on GitHub.
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- Award ID(s):
- 1664172
- PAR ID:
- 10296041
- Date Published:
- Journal Name:
- 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
- Page Range / eLocation ID:
- 385 to 389
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISBI48211.2021.9433831
