Logo image
Back
Deep Neural Networks for Surface Segmentation Meet Conditional Random Fields
Preprint   Open access

Deep Neural Networks for Surface Segmentation Meet Conditional Random Fields

Leixin Zhou, Zisha Zhong, Abhay Shah, Bensheng Qiu, John Buatti and Xiaodong Wu
ArXiv.org
06/11/2019
DOI: 10.48550/arXiv.1906.04714
url
https://arxiv.org/abs/1906.04714View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Automated surface segmentation is important and challenging in many medical image analysis applications. Recent deep learning based methods have been developed for various object segmentation tasks. Most of them are a classification based approach (e.g., U-net), which predicts the probability of being target object or background for each voxel. One problem of those methods is lacking of topology guarantee for segmented objects, and usually post processing is needed to infer the boundary surface of the object. In this paper, a novel model based on 3-D convolutional neural networks (CNNs) and Conditional Random Fields (CRFs) is proposed to tackle the surface segmentation problem with end-to-end training. To the best of our knowledge, this is the first study to apply a 3-D neural network with a CRFs model for direct surface segmentation. Experiments carried out on NCI-ISBI 2013 MR prostate dataset and Medical Segmentation Decathlon Spleen dataset demonstrated promising segmentation results.

Details

Metrics

36 Record Views
Logo image