Medical Image Segmentation via Unsupervised Convolutional Neural Network

• URL: http://arxiv.org/abs/2001.10155v4
• Date: Mon, 6 Jul 2020 16:09:09 GMT
• Title: Medical Image Segmentation via Unsupervised Convolutional Neural Network
• Authors: Junyu Chen, Eric C. Frey
• Abstract summary: We present a novel learning-based segmentation model that could be trained semi- or un- supervised. We parameterize the Active contour without edges (ACWE) framework via a convolutional neural network (ConvNet) We show that the method provides fast and high-quality bone segmentation in the context of single-photon emission computed tomography (SPECT) image.
• Score: 1.6396833577035679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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