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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Rutter, E.M., Lagergren, J.H., Flores, K.B. (2019). A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation. In: Wang, Q., et al. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART MIL3ID 2019 2019. Lecture Notes in Computer Science(), vol 11795. Springer, Cham. https://doi.org/10.1007/978-3-030-33391-1_22
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