دورية أكاديمية
Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference
| العنوان: | Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference |
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| المؤلفون: | Curzio Checcucci, Bridget Wicinski, Giacomo Mazzamuto, Marina Scardigli, Josephine Ramazzotti, Niamh Brady, Francesco S. Pavone, Patrick R. Hof, Irene Costantini, Paolo Frasconi |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Cell detection, Deep-learning, Human brain, Broca’s area, 3D reconstruction, Fluorescence microscopy, Medicine, Science |
| الوصف: | Abstract 3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation. However, accurate quantification of neurons in the human brain presents specific challenges, such as high pixel intensity variability, autofluorescence, non-specific fluorescence and very large size of data. In this paper, we provide a thorough empirical evaluation of three techniques based on deep learning (StarDist, CellPose and BCFind-v2, an updated version of BCFind) using a recently introduced three-dimensional stereological design as a reference for large-scale insights. As a representative problem in human brain analysis, we focus on a $$4~\text {-cm}^3$$ 4 -cm 3 portion of the Broca’s area. We aim at helping users in selecting appropriate techniques depending on their research objectives. To this end, we compare methods along various dimensions of analysis, including correctness of the predicted density and localization, computational efficiency, and human annotation effort. Our results suggest that deep learning approaches are very effective, have a high throughput providing each cell 3D location, and obtain results comparable to the estimates of the adopted stereological design. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-65092-3 |
| URL الوصول: | https://doaj.org/article/7a03e70b94084892bc125093f3c41c3b |
| رقم الأكسشن: | edsdoj.7a03e70b94084892bc125093f3c41c3b |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-65092-3 |