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Instance Segmentation of Neural Cells

  • Jingru YiEmail author
  • Pengxiang Wu
  • Menglin Jiang
  • Daniel J. Hoeppner
  • Dimitris N. Metaxas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)

Abstract

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 performance in terms of accuracy and efficiency, comparing favorably with current state-of-the-art methods.

Keywords

Neural cell Instance segmentation Cell detection Cell segmentation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceRutgers UniversityPiscatawayUSA
  2. 2.Astellas Research Institute of AmericaSan DiegoUSA

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