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UOLO - Automatic Object Detection and Segmentation in Biomedical Images

  • Teresa Araújo
  • Guilherme Aresta
  • Adrian Galdran
  • Pedro Costa
  • Ana Maria Mendonça
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11045)

Abstract

We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.

Keywords

Detection Segmentation Biomedical images Eye fundus images Convolutional neural networks 

Notes

Acknowledgements

T. Araújo is funded by the FCT grant SFRH/BD/122365/ 2016. G. Aresta is funded by the FCT grant SFRH/BD/120435/2016. This work is funded by the ERDF European Regional Development Fund, Operational Programme for Competitiveness and Internationalisation - COMPETE 2020, and by National Funds through the FCT - project CMUP-ERI/TIC/0028/2014.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Teresa Araújo
    • 1
    • 2
  • Guilherme Aresta
    • 1
    • 2
  • Adrian Galdran
    • 1
  • Pedro Costa
    • 1
  • Ana Maria Mendonça
    • 1
    • 2
  • Aurélio Campilho
    • 1
    • 2
  1. 1.INESC TEC - Institute for Systems and Computer Engineering, Technology and SciencePortoPortugal
  2. 2.Faculdade de Engenharia da Universidade do PortoPortoPortugal

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