2026-04-23T05:02:36Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/157312024-09-27T08:01:38Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19605

00925njm 22002777a 4500 dc Molina-Moreno, Miguel author González-Díaz, Iván author Sicilia, Jon author Crainiciuc, Georgiana author Palomino-Segura, Miguel author Hidalgo, Andres author Díaz-de-María, Fernando author 2022-04 Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances. Med Image Anal. 2022 Apr;77:102358 http://hdl.handle.net/20.500.12105/15731 35066392 10.1016/j.media.2022.102358 1361-8423 Medical image analysis ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.