Co-localization of fluorescent signals using deep learning with Manders overlapping coefficient

Yimeng Dou, Yi Hua Tsai, Chih Chieh Liu, Brad A. Hobson, Pamela J. Lein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Object-based co-localization of uorescent signals allows the assessment of interactions between two (or more) biological entities using spatial information. It relies on object identification with high accuracy to separate uorescent signals from the background. Object detectors using convolutional neural networks (CNN) with annotated training samples could facilitate the process by detecting and counting fluorescent-labeled cells from uorescence photomicrographs. However, datasets containing segmented annotations of colocalized cells are generally not available, and creating a new dataset with delineated masks is label-intensive. Also, the colocalization coefficient is often not used as a component during training with the CNN model. Yet, it may aid with localizing and detecting objects during training and testing. In this work, we propose to address these issues by using a quantification coefficient for co-localization called Manders overlapping coefficient (MOC)1 as a single-layer branch in a CNN. Fully convolutional one-state (FCOS)2 with a Resnet101 backbone served as the network to evaluate the effectiveness of the novel branch to assist with bounding box prediction. Training data were sourced from lab curated uorescence images of neurons from the rat hippocampus, piriform cortex, somatosensory cortex, and amygdala. Results suggest that using modified FCOS with MOC outperformed the original FCOS model for accuracy in detecting uorescence signals by 1.1% in mean average precision (mAP). The model could be downloaded from https://github.com/Alphafrey946/Colocalization-MOC.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510640214
DOIs
StatePublished - 2021
Externally publishedYes
EventMedical Imaging 2021: Image Processing - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11596
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Image Processing
Country/TerritoryUnited States
CityVirtual, Online
Period2/15/212/19/21

Keywords

  • Co-localization
  • Deep learning
  • Fluorescence microscopy
  • High-content screening
  • Object Detection
  • Pattern recognition and classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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