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2019 | OriginalPaper | Chapter

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

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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.
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Metadata
Title
A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation
Authors
Erica M. Rutter
John H. Lagergren
Kevin B. Flores
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_22

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