Browsing by Author "Silveira, Margarida"
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- 3DCellPol: joint detection and pairing of cell structures to compute cell polarityPublication . Narotamo, Hemaxi; Franco, Cláudio A.; Silveira, MargaridaCell polarity is essential for tissue structure and cell migration, and its dysregulation is linked to diseases such as cancer and vascular disorders. Understanding the associations between molecular mechanisms, such as genetic defects, and abnormal cell polarization can provide clinicians with valuable biomarkers for early disease diagnosis and lead to more targeted therapeutic interventions. Here, we present a deep-learning framework for cell polarity computation based on the association between pairs of objects. Our approach, named 3DCellPol, is trained to detect and group the centroids of two distinct objects. To demonstrate the potential of 3DCellPol, we use it to compute cell polarity by pairing two cell organelles: nuclei and Golgi. The vectors between nuclei and Golgi define the front-rear polarity axis in endothelial cells. 3DCellPol was evaluated on 3D microscopy images of mouse retinas. It detected 71% of the nucleus–Golgi vectors and outperformed previous methods while requiring much less supervision. Moreover, incorporating synthetic data generated by a generative adversarial network further improved detection to 78%. We additionally demonstrated our model's adaptability to 2D images by applying it to a public dataset of cervical cytology images, where polarity is defined based on the cytoplasm-nucleus vectors. In this dataset, our model detected over 90% of vectors. 3DCellPol's ability to robustly compute cell polarity is crucial for understanding mechanisms of diseases where abnormal polarity plays a key role, and it may contribute to improved diagnostics and enable targeted therapies. Hence, it is a valuable open-source tool for both biomedical research and clinical practice.
- 3DVascNet: an automated software for segmentation and quantification of mouse vascular networks in 3DPublication . Narotamo, Hemaxi; Silveira, Margarida; Franco, Cláudio A.BACKGROUND: Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2-dimensional projections of 3-dimensional (3D) networks, a strategy that has several obvious shortcomings. For instance, it does not capture the true geometry of the vasculature and generates artifacts on vessel connectivity. These limitations are accepted in the field because manual analysis of 3D vascular networks is a laborious and complex process that is often prohibitive for large volumes. METHODS: To overcome these issues, we developed 3DVascNet, a deep learning–based software for automated segmentation and quantification of 3D retinal vascular networks. 3DVascNet performs segmentation based on a deep learning model, and it quantifies vascular morphometric parameters such as vessel density, branch length, vessel radius, and branching point density. We tested the performance of 3DVascNet using a large data set of 3D microscopy images of mouse retinal blood vessels. RESULTS: We demonstrated that 3DVascNet efficiently segments vascular networks in 3D and that vascular morphometric parameters capture phenotypes detected by using manual segmentation and quantification in 2 dimension. In addition, we showed that, despite being trained on retinal images, 3DVascNet has high generalization capability and successfully segments images originating from other data sets and organs. CONCLUSIONS: Overall, we present 3DVascNet, a freely available software that includes a user-friendly graphical interface for researchers with no programming experience, which will greatly facilitate the ability to study vascular networks in 3D in health and disease. Moreover, the source code of 3DVascNet is publicly available, thus it can be easily extended for the analysis of other 3D vascular networks by other users.