Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network
| العنوان: | Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network |
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| المؤلفون: | Maryse Lapierre-Landry, Shan Ling, David L. Wilson, Michael W. Jenkins, Mahdi Bayat, Zexuan Liu |
| المصدر: | IEEE access : practical innovations, open solutions IEEE Access, Vol 9, Pp 60396-60408 (2021) |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0301 basic medicine, Point spread function, General Computer Science, Computer science, Image processing, Article, spatial statistics, 03 medical and health sciences, 0302 clinical medicine, Microscopy, General Materials Science, cell detection, Spatial analysis, cell segmentation, business.industry, whole tissue, Deep learning, Resolution (electron density), General Engineering, Centroid, deep learning, Pattern recognition, Image segmentation, TK1-9971, centroid detection, 030104 developmental biology, 3D microscopy, regression, Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, business, V-net, 030217 neurology & neurosurgery |
| الوصف: | Advances in three-dimensional microscopy and tissue clearing are enabling whole-organ imaging with single-cell resolution. Fast and reliable image processing tools are needed to analyze the resulting image volumes, including automated cell detection, cell counting and cell analytics. Deep learning approaches have shown promising results in two- and three-dimensional nuclei detection tasks, however detecting overlapping or non-spherical nuclei of different sizes and shapes in the presence of a blurring point spread function remains challenging and often leads to incorrect nuclei merging and splitting. Here we present a new regression-based fully convolutional network that located a thousand nuclei centroids with high accuracy in under a minute when combined with V-net, a popular three-dimensional semantic-segmentation architecture. High nuclei detection F1-scores of 95.3% and 92.5% were obtained in two different whole quail embryonic hearts, a tissue type difficult to segment because of its high cell density, and heterogeneous and elliptical nuclei. Similar high scores were obtained in the mouse brain stem, demonstrating that this approach is highly transferable to nuclei of different shapes and intensities. Finally, spatial statistics were performed on the resulting centroids. The spatial distribution of nuclei obtained by our approach most resembles the spatial distribution of manually identified nuclei, indicating that this approach could serve in future spatial analyses of cell organization. |
| اللغة: | English |
| تدمد: | 2169-3536 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ae073af21f2a56116024a40b40724cd http://europepmc.org/articles/PMC8751907 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....4ae073af21f2a56116024a40b40724cd |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21693536 |
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