https://github.com/fperdigon/DeepVertebralLabeling_RV

Deep learning model for vertebral labeling on MRI
https://github.com/fperdigon/DeepVertebralLabeling_RV

automatic-labelling deep-learning keras-tensorflow mri-images vertebra

Last synced: 7 months ago
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Deep learning model for vertebral labeling on MRI

• Host: GitHub
• URL: https://github.com/fperdigon/DeepVertebralLabeling_RV
• Owner: fperdigon
• Created: 2018-08-27T16:17:53.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-08-27T18:47:25.000Z (about 7 years ago)
• Last Synced: 2024-07-31T20:44:00.123Z (about 1 year ago)
• Topics: automatic-labelling, deep-learning, keras-tensorflow, mri-images, vertebra
• Language: Python
• Homepage:
• Size: 167 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• cuban-opensource - DeepVertebralLabeling

README

          
# DeepVertebralLabeling_RV

![DeepVertebralLabeling_Net overview](/img/DeepVertebralLabeling_Net.png "DeepVertebralLabeling_Net overview")

### Overview



For the diagnosis and monitoring of various diseases in the spine and the central nervous system, the detection and labeling of vertebrae in magnetic resonance imaging (MRI) is useful. Although several automatic methods for the detection and labeling of vertebrae have been developed, this is still an open task in which many improvements can be made. One way to detect the vertebra is to focus on the intervertebral discs (IVD), which are natural spacers between vertebrae. In this work, we present a set of convolutional networks (CNN) that perform regression for the detection and labeling of the IVD. The entry for each of the CNNs is the midsagittal plane image of the acquired volume. Each CNN is in charge of detecting one intervertebral disc, so the labeling is done implicitly. The output of each CNN is the coordinate (x, y) of the located IVD. We have done the training with the first 6 IVD (C2-C3 to C7-T1) using a total of 631 images with a pixel resolution of 1mm x 1mm. The mean error is between 2.98.mm and 4.33mm, the standard deviation range is between ±2.45 and ±3.45. Such results are competitive with the state of the art but require significantly less computational resources (estimated 2x) than other architectures based on fully convolutional networks.



Error distribution:












For more info please visit: [DeepVertebralLabeling_RV](https://www.researchgate.net/publication/326840620_Vertebral_labeling_on_MRI_using_deep_learning_techniques)

In this repository you can find the code for the model in Keras/Tensorflow.

### Citing DeepVertebralLabeling_RV

When citing DeepVertebralLabeling_VR in academic papers and thesis, please use this BibTeX entry:




    @inproceedings{Romero2018,

    address = {Montreal, QC, Canada},

    author = {Romero, Francisco Perdigon and David, Jean-Pierre and Cohen-Adad, Julien},

    booktitle = {NeuroInformatics 2018},

    doi = {10.13140/RG.2.2.33723.92962},

    title = {{Vertebral labeling on MRI using deep learning techniques}},

    url = {https://www.researchgate.net/publication/326840620{\_}Vertebral{\_}labeling{\_}on{\_}MRI{\_}using{\_}deep{\_}learning{\_}techniques},

    year = {2018}

    }

### License

MIT License 


Copyright (c) 2018 Francisco Perdigon Romero



Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:



The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

SOFTWARE.