.png){kind=icon}
Return
Active Structured Learning for Cell Tracking: Algorithm, Framework, and Usability
.png){kind=icon}
PRE
AI
DOI：10.1109/TMI.2013.2296937.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
One distinguishing property of life is its temporal dynamics, and it is hence only natural that time lapse experiments play a crucial role in modern biomedical research areas such as signaling pathways, drug discovery or developmental biology. Such experiments yield a very large number of images that encode complex cellular activities, and reliable automated cell tracking emerges naturally as a prerequisite for further quantitative analysis. However, many existing cell tracking methods are restricted to using only a small number of features to allow for manual tweaking. In this paper, we propose a novel cell tracking approach that embraces a powerful machine learning technique to optimize the tracking parameters based on user annotated tracks. Our approach replaces the tedious parameter tuning with parameter learning and allows for the use of a much richer set of complex tracking features, which in turn affords superior prediction accuracy. Furthermore, we developed an active learning approach for efficient training data retrieval, which reduces the annotation effort to only 17%. In practical terms, our approach allows life science researchers to inject their expertise in a more intuitive and direct manner. This process is further facilitated by using a glyph visualization technique for ground truth annotation and validation. Evaluation and comparison on several publicly available benchmark sequences show significant performance improvement over recently reported approaches. Code and software tools are provided to the public.
Keywords:
Active learning
cell tracking
glyph visualization
integer linear programming
machine learning
mitosis detection
structured prediction
tracking features
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.8
Papers:
6.2K
Citations:
3.7W
Organization
Cited Papers
Increased Bacillus Calmette-Guérin Treatment Intensity Associated With Improved Outcomes in Elderly Patients With Non–Muscle-invasive Bladder Cancer in United States Clinical Practice.png)
Urology
UrologyIF0
PREAI
ShareSave
ShareSavePTX encapsulated by an XG–DOX conjugate for combination therapy against multi-drug resistanceRSC Advances
RSC AdvancesIF0
PREAI
ShareSaveShareSaveShareSave
ShareSave
View more