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arXiv · 1705.06260

A deep level set method for image segmentation

Min Tang·Sepehr Valipour·Zichen Vincent Zhang·Dana Cobzas·MartinJagersand
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Abstract

This paper proposes a novel image segmentation approachthat integrates fully convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the integrated method can incorporatesmoothing and prior information to achieve an accurate segmentation.Furthermore, different than using the level set model as a post-processingtool, we integrate it into the training phase to fine-tune the FCN. Thisallows the use of unlabeled data during training in a semi-supervisedsetting. Using two types of medical imaging data (liver CT and left ven-tricle MRI data), we show that the integrated method achieves goodperformance even when little training data is available, outperformingthe FCN or the level set model alone.

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Min Tang, Sepehr Valipour, Zichen Vincent Zhang, Dana Cobzas, MartinJagersand. 2017-05-17. A deep level set method for image segmentation. https://arxiv.org/abs/1705.06260

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