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@article{Smith:2023:10.1016/j.bpj.2023.03.038,
author = {Smith, M and Sparks, H and Almagro, J and Chaigne, A and Behrens, A and Dunsby, C and Salbreux, G},
doi = {10.1016/j.bpj.2023.03.038},
journal = {Biophysical Journal},
pages = {1586--1599},
title = {Active mesh and neural network pipeline for cell aggregate segmentation},
url = {http://dx.doi.org/10.1016/j.bpj.2023.03.038},
volume = {122},
year = {2023}
}
TY - JOUR
AB - Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary gland organoids imaged over 24 h with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin that implements active mesh deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction.
AU - Smith,M
AU - Sparks,H
AU - Almagro,J
AU - Chaigne,A
AU - Behrens,A
AU - Dunsby,C
AU - Salbreux,G
DO - 10.1016/j.bpj.2023.03.038
EP - 1599
PY - 2023///
SN - 0006-3495
SP - 1586
TI - Active mesh and neural network pipeline for cell aggregate segmentation
T2 - Biophysical Journal
UR - http://dx.doi.org/10.1016/j.bpj.2023.03.038
UR - https://www.sciencedirect.com/science/article/pii/S0006349523002114
UR - http://hdl.handle.net/10044/1/103554
VL - 122
ER -