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Medical Image Segmentation via Unsupervised Convolutional Neural Network

2020-04-13 19:29:41
Junyu Chen, Eric C. Frey
arXiv_CV
             
Abstract

For the majority of the learning-based segmentation methods, a large quantity of high-quality training data is required. In this paper, we present a novel learning-based segmentation model that could be trained semi- or un- supervised. Specifically, in the unsupervised setting, we parameterize the Active contour without edges (ACWE) framework via a convolutional neural network (ConvNet), and optimize the parameters of the ConvNet using a self-supervised method. In another setting (semi-supervised), the auxiliary segmentation ground truth is used during training. We show that the method provides fast and high-quality bone segmentation in the context of single-photon emission computed tomography (SPECT) image.

Abstract (translated)

URL

https://arxiv.org/abs/2001.10155

PDF

https://arxiv.org/pdf/2001.10155.pdf

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