Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation-Reference-Cited by-同舟云学术

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Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

Published:2018-05-04 Issue:9 Volume:63 Page:095017
ISSN:1361-6560
Container-title:Physics in Medicine & Biology
language:
Short-container-title:Phys. Med. Biol.

Author:

Qin Wenjian,Wu Jia,Han Fei,Yuan Yixuan,Zhao Wei^ORCID,Ibragimov Bulat,Gu Jia,Xing Lei

Funder

The Faculty Research Award from Google Inc.

National Natural Science Foundation of China

Shenzhen basic technology research project

NIH/NCI

Guangdong province science and technology plan projects

Publisher

IOP Publishing

Subject

Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://iopscience.iop.org/article/10.1088/1361-6560/aabd19/pdf

Reference45 articles.

1. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

2. Big Data and machine learning in radiation oncology: State of the art and future prospects

3. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

4. Liver Segmentation on CT and MR Using Laplacian Mesh Optimization

5. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

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