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A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation

  • Erica M. RutterEmail author
  • John H. Lagergren
  • Kevin B. Flores
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

Obtaining large amounts of annotated biomedical data to train convolutional neural networks (CNNs) for image segmentation is expensive. We propose a method that requires only a few segmentation examples to accurately train a semi-automated segmentation algorithm. Our algorithm, a convolutional neural network method for boundary optimization (CoMBO), can be used to rapidly outline object boundaries using orders of magnitude less annotation than full segmentation masks, i.e., only a few pixels per image. We found that CoMBO is significantly more accurate than state-of-the-art machine learning methods such as Mask R-CNN. We also show how we can use CoMBO predictions, when CoMBO is trained on just 3 images, to rapidly create large amounts of accurate training data for Mask R-CNN. Our few-shot method is demonstrated on ISBI cell tracking challenge datasets.

Keywords

Biomedical image segmentation Few shot learning Convolutional neural network 

Supplementary material

490967_1_En_22_MOESM1_ESM.pdf (194 kb)
Supplementary material 1 (pdf 194 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Erica M. Rutter
    • 1
    • 2
    Email author
  • John H. Lagergren
    • 1
  • Kevin B. Flores
    • 1
  1. 1.Center for Research in Scientific Computation, Department of MathematicsNorth Carolina State UniversityRaleighUSA
  2. 2.Department of Applied MathematicsUniversity of California, MercedMercedUSA

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