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| Item Type: | Conference or Workshop Item |
|---|---|
| Title: | Simultaneous lung cell and nucleus segmentation from labelled versus unlabelled human lung DIC images |
| Creators Name: | Dohmen, M., Mittermaier, M., Rumberger, J.L., Yang, L.L., Gruber, A.D., Toennies, M., Hippenstiel, S., Kainmueller, D. and Hocke, A.C. |
| Abstract: | For high-throughput and quantitative analyses of tissue microscopy images such as tissue morphology, cell type allocation and counting, simultaneous semantic and instance segmentation is needed. However, only few public data is available for developing such methods. Therefore, we provide a data set of differential interference contrast (DIC) images of human lung tissue complemented by multiplex fluorescence labelling including semantic, cell and nucleus instance annotations. We examined the structures and tasks for which fluorescence labeling is essential and compared one set of deep neural networks (DNNs) trained solely on unlabeled DIC images with another set that also utilized fluorescence labeling. Our findings indicate that while fluorescence labeling is crucial for the detection of the majority of cell nuclei, certain cells and tissue compartments can be quantified without it. Our analysis also extends currently limited knowledge about cell type composition in normal human lung tissue and contributes to further advances in quantitative tissue characterization in health and disease. |
| Keywords: | Semantic Instance Segmentation, Cell and Nuclei Segmentation, Fluorescence Labelling, Human Lung Tissue, Alveolar Type 2 Cells, Alveolar Macrophages |
| Source: | 2024 IEEE International Symposium on Biomedical Imaging (ISBI) |
| Title of Book: | 2024 IEEE International Symposium on Biomedical Imaging (ISBI) |
| ISSN: | 1945-7928 |
| ISBN: | 979-8-3503-1333-8 |
| Publisher: | IEEE |
| Page Range: | 1-5 |
| Number of Pages: | 5 |
| Date: | 22 August 2024 |
| Official Publication: | https://doi.org/10.1109/isbi56570.2024.10635198 |
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