ArticleJournal of digital imaging2020
Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.Scientific reports · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
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- Spatial Transcriptomic Analyses of Spermatogenesis.Methods in molecular biology (Clifton, N.J.) · 2025Article
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- Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN.Bioengineering (Basel, Switzerland) · 2024Article
- Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images.Bioinformatics and biology insights · 2024Article
- Whole-Slide Imaging, Mutual Information Registration for Multiplex Immunohistochemistry and Immunofluorescence.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- Experimental Study on Bubble Dynamics and Mass Transfer Characteristics of Coaxial Bubbles in Petroleum-Based Liquids.ACS omega · 2023Article
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space.PloS one · 2023Article
- DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation.PloS one · 2023Article
- The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient.Sensors (Basel, Switzerland) · 2022Article
- SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images.BMC bioinformatics · 2022Article
- Convolutional Blur Attention Network for Cell Nuclei Segmentation.Sensors (Basel, Switzerland) · 2022Article
- Deep learning provides high accuracy in automated chondrocyte viability assessment in articular cartilage using nonlinear optical microscopy.Biomedical optics express · 2021Article
- Diffusion Tensor Imaging Features of Watershed Segmentation Algorithm for Analysis of the Relationship between Depression and Brain Nerve Function of Patients with End-Stage Renal Disease.Journal of healthcare engineering · 2021Article
- A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients.Journal of healthcare engineering · 2021Article
- Graph-Theoretic Post-Processing of Segmentation With Application to Dense Biofilms.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2021Article
- Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations.Computational and mathematical methods in medicine · 2021Article
- Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential.Patterns (New York, N.Y.) · 2020Review
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Morphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei segmentation presents many challenges because they usually create complex clusters in cytological samples. To deal with this problem, we are proposing an approach, which combines convolutional neural network and watershed transform to segment nuclei in cytological images of breast cancer. The method initially is preprocessing images using color deconvolution to highlight hematoxylin-stained objects (nuclei). Next, convolutional neural network is applied to perform semantic segmentation of preprocessed image. It finds nuclei areas, cytoplasm areas, edges of nuclei, and background. All connected components in the binary mask of nuclei are treated as potential nuclei. However, some objects actually are clusters of overlapping nuclei. They are detected by their outlying values of morphometric features. Then an attempt is made to separate them using the seeded watershed segmentation. If the attempt is successful, they are included in the nuclei set. The accuracy of this approach is evaluated with the help of referenced, manually segmented images. The degree of matching between reference nuclei and discovered objects is measured with the help of Jaccard distance and Hausdorff distance. As part of the study, we verified how the use of a convolutional neural network instead of the intensity thresholding to generate a topographical map for the watershed improves segmentation outcomes. Our results show that convolutional neural network outperforms Otsu thresholding and adaptive thresholding in most cases, especially in scenarios with many overlapping nuclei.
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