Difference between revisions of "Publications/puybareau.18.brainles"
From LRDE
(Created page with "{{Publication | published = true | date = 2018-11-05 | authors = Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio | title = Segmentation of gliomas and...") |
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| date = 2018-11-05 |
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| authors = Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio |
| authors = Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio |
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− | | title = Segmentation of |
+ | | title = Segmentation of Gliomas and Prediction of Patient Overall Survival: A Simple and Fast Procedure. |
| booktitle = Proceedings of the Workshop on Brain Lesions (BrainLes)in conjunction with MICCAI |
| booktitle = Proceedings of the Workshop on Brain Lesions (BrainLes)in conjunction with MICCAI |
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| series = Lecture Notes in Computer Science |
| series = Lecture Notes in Computer Science |
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author = <nowiki>{</nowiki>\'Elodie Puybareau and Guillaume Tochon and Joseph |
author = <nowiki>{</nowiki>\'Elodie Puybareau and Guillaume Tochon and Joseph |
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Chazalon and Jonathan Fabrizio<nowiki>}</nowiki>, |
Chazalon and Jonathan Fabrizio<nowiki>}</nowiki>, |
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− | title = <nowiki>{</nowiki>Segmentation of |
+ | title = <nowiki>{</nowiki>Segmentation of Gliomas and Prediction of Patient Overall |
− | + | Survival: <nowiki>{</nowiki>A<nowiki>}</nowiki> Simple and Fast Procedure.<nowiki>}</nowiki>, |
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booktitle = <nowiki>{</nowiki>Proceedings of the Workshop on Brain Lesions (BrainLes), |
booktitle = <nowiki>{</nowiki>Proceedings of the Workshop on Brain Lesions (BrainLes), |
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in conjunction with MICCAI<nowiki>}</nowiki>, |
in conjunction with MICCAI<nowiki>}</nowiki>, |
Revision as of 11:56, 26 November 2018
- Authors
- Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio
- Where
- Proceedings of the Workshop on Brain Lesions (BrainLes)in conjunction with MICCAI
- Type
- inproceedings
- Publisher
- Springer
- Projects
- Olena
- Keywords
- Image
- Date
- 2018-11-05
Abstract
In this paper, we propose a fast automatic method that seg- ments glioma without any manual assistance, using a fully convolutional network (FCN) and transfer learning. From this segmentation, we predict the patient overall survival using only the results of the segmentation and a home made atlas. The FCN is the base network of VGG-16pretrained on ImageNet for natural image classificationand fine tuned with the training dataset of the MICCAI 2018 BraTS Challenge. It relies on the "pseudo-3D" method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes. For each n th slice of the volume to segment, we consider three images, corresponding to the (n-1)th, nthand (n-1)th slices of the original volume. These three gray-level 2D images are assembled to form a 2D RGB color image (one image per channel). This image is the input of the FCN to obtain a 2D segmentation of the n th slice. We process all slices, then stack the results to form the 3D output segmentation. With such a technique, the segmentation of a 3D volume takes only a few seconds. The prediction is based on Random Forests, and has the advantage of not being dependant of the acquisition modality, making it robust to inter-base data.
Bibtex (lrde.bib)
@InProceedings{ puybareau.18.brainles, author = {\'Elodie Puybareau and Guillaume Tochon and Joseph Chazalon and Jonathan Fabrizio}, title = {Segmentation of Gliomas and Prediction of Patient Overall Survival: {A} Simple and Fast Procedure.}, booktitle = {Proceedings of the Workshop on Brain Lesions (BrainLes), in conjunction with MICCAI}, year = 2018, series = {Lecture Notes in Computer Science}, publisher = {Springer}, abstract = {In this paper, we propose a fast automatic method that seg- ments glioma without any manual assistance, using a fully convolutional network (FCN) and transfer learning. From this segmentation, we predict the patient overall survival using only the results of the segmentation and a home made atlas. The FCN is the base network of VGG-16, pretrained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 BraTS Challenge. It relies on the "pseudo-3D" method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes. For each n th slice of the volume to segment, we consider three images, corresponding to the (n-1)th, nth, and (n-1)th slices of the original volume. These three gray-level 2D images are assembled to form a 2D RGB color image (one image per channel). This image is the input of the FCN to obtain a 2D segmentation of the n th slice. We process all slices, then stack the results to form the 3D output segmentation. With such a technique, the segmentation of a 3D volume takes only a few seconds. The prediction is based on Random Forests, and has the advantage of not being dependant of the acquisition modality, making it robust to inter-base data.} }