Exploring the Role of Deep Learning in Liver Segmentation
Author:
Affiliation:
1. City University of Hong Kong,Hong Kong,China
2. Lixin Pharmaceuticals,Guangzhou,China
3. City University of Hong Kong Shenzhen Research Institute,Shenzhen,China
4. Shenzhen Institute for Advanced Study, UESTC,Shenzhen,China
Funder
Guangzhou Municipal Science and Technology Bureau
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10164832/10164812/10165029.pdf?arnumber=10165029
Reference21 articles.
1. Constructing a Probabilistic Model for Automated Liver Region Segmentation Using Non-contrast X-Ray Torso CT images
2. Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection
3. Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts
4. Fully automatic liver segmentation through graph-cut technique, Engineering in Medicine and Biology Society, 2007. EMBS 2007;massoptier;29th Annual International Conference of the IEEE,2007
5. Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation;iglovikov;arXiv preprint arXiv 1801 05078,2018
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