Computational segmentation of nuclei and vacuoles based on statistical inference for tomographic phase microscopy in flow cytometry
Identifying intracellular organelles within the 3D label-free tomograms of cells’ refractive indexes recorded in flow cytometry is challenging. Here we present a method for the 3D statistical segmentation of nuclei and vacuoles in flowing cells.
2-s2.0-85210371708
National Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”
National Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”
Università degli Studi di Napoli Federico II
École Polytechnique Fédérale de Lausanne
National Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”
National Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”
National Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”
CEINGE-Biotecnologie Avanzate S.c.a r.l.
CEINGE-Biotecnologie Avanzate S.c.a r.l.
Università degli Studi di Napoli Federico II
2024
REVIEWED
EPFL
| Event name | Event acronym | Event place | Event date |
Paestum, Italy | 2024-06-03 - 2024-06-06 | ||