Attentive neural cell instance segmentation

Jingru Yi, Pengxiang Wu, Menglin Jiang, Qiaoying Huang, Daniel J. Hoeppner, Dimitris N. Metaxas

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Neural cell instance segmentation, which aims at joint detection and segmentation of every neural cell in a microscopic image, is essential to many neuroscience applications. The challenge of this task involves cell adhesion, cell distortion, unclear cell contours, low-contrast cell protrusion structures, and background impurities. Consequently, current instance segmentation methods generally fall short of precision. In this paper, we propose an attentive instance segmentation method that accurately predicts the bounding box of each cell as well as its segmentation mask simultaneously. In particular, our method builds on a joint network that combines a single shot multi-box detector (SSD) and a U-net. Furthermore, we employ the attention mechanism in both detection and segmentation modules to focus the model on the useful features. The proposed method is validated on a dataset of neural cell microscopic images. Experimental results demonstrate that our approach can accurately detect and segment neural cell instances at a fast speed, comparing favorably with the state-of-the-art methods. Our code is released on GitHub. The link is https://github.com/yijingru/ANCIS-Pytorch.

Original languageEnglish (US)
Pages (from-to)228-240
Number of pages13
JournalMedical Image Analysis
Volume55
DOIs
StatePublished - Jul 2019

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Keywords

  • Cell detection
  • Cell segmentation
  • Instance segmentation
  • Neural cell

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