Accurate cell instance segmentation plays an important role in
the study of neural cell interactions, which are critical for understanding
the development of brain. These interactions are
performed through the filopodia and lamellipodia of neural
cells, which are extremely tiny structures and as a result render
most existing instance segmentation methods powerless to
precisely capture them. To solve this issue, in this paper we
present a novel hierarchical neural network comprising object
detection and segmentation modules. Compared to previous
work, our model is able to efficiently share and make full use
of the information at different levels between the two modules.
Our method is simple yet powerful, and experimental
results show that it captures the contours of neural cells, especially
the filopodia and lamellipodia, with high accuracy,
and outperforms recent state of the art by a large margin.
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Instance Segmentation of Neural Cells
Instance segmentation of neural cells plays an important role in brain study. However, this task is challenging due to the special shapes and behaviors of neural cells. Existing methods are not precise enough to capture their tiny structures, e.g., filopodia and lamellipodia, which are critical to the understanding of cell interaction and behavior. To this end, we propose a novel deep multi-task learning model to jointly detect and segment neural cells instance-wise. Our method is built upon SSD, with ResNet101 as the backbone to achieve both high detection accuracy and fast speed. Furthermore, unlike existing works which tend to produce wavy and inaccurate boundaries, we embed a deconvolution module into SSD to better capture details. Experiments on a dataset of neural cell microscopic images show that our method is able to achieve better per- formance in terms of accuracy and efficiency, comparing favorably with current state-of-the-art methods.
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- Award ID(s):
- 1747778
- NSF-PAR ID:
- 10105319
- Date Published:
- Journal Name:
- ECCV 2018 workshop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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