Files and links (1)
SubmittedarXiv.org - Non-exclusive license to distribute, Open
Journal article
Instance-Level Microtubule Tracking
IEEE transactions on medical imaging, v 39(6), pp 2061-2075
Jun 2020
PMID: 31905134
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We propose a new method of instance-level microtubule (MT) tracking in time-lapse image series using recurrent attention. Our novel deep learning algorithm segments individual MTs at each frame. Segmentation results from successive frames are used to assign correspondences among MTs. This ultimately generates a distinct path trajectory for each MT through the frames. Based on these trajectories, we estimate MT velocities. To validate our proposed technique, we conduct experiments using real and simulated data. We use statistics derived from real time-lapse series of MT gliding assays to simulate realistic MT time-lapse image series in our simulated data. This data set is employed as pre-training and hyperparameter optimization for our network before training on the real data. Our experimental results show that the proposed supervised learning algorithm improves the precision for MT instance velocity estimation drastically to 71.3% from the baseline result (29.3%). We also demonstrate how the inclusion of temporal information into our deep network can reduce the false negative rates from 67.8% (baseline) down to 28.7% (proposed). Our findings in this work are expected to help biologists characterize the spatial arrangement of MTs, specifically the effects of MT-MT interactions.
Metrics
Details
- Title
- Instance-Level Microtubule Tracking
- Creators
- Samira Masoudi - University of WyomingAfsaneh Razi - Department of Computer Science, University of Central Florida, Orlando, FL, USACameron H. G. Wright - University of WyomingJesse C. Gatlin - University of WyomingUlas Bagci - Department of Computer Science, University of Central Florida, Orlando, FL, USA
- Publication Details
- IEEE transactions on medical imaging, v 39(6), pp 2061-2075
- Publisher
- IEEE
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000544923000024
- Scopus ID
- 2-s2.0-85085905440
- Other Identifier
- 991021861656404721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology
- Radiology, Nuclear Medicine & Medical Imaging