# Difference between revisions of "Publications/carlinet.19.csi"

### From LRDE

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| title = Intervertebral Disc Segmentation Using Mathematical Morphology—A CNN-Free Approach |
| title = Intervertebral Disc Segmentation Using Mathematical Morphology—A CNN-Free Approach |
||

| booktitle = Proceedings of the 5th MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging (CSI) |
| booktitle = Proceedings of the 5th MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging (CSI) |
||

− | | publisher = Springer |
+ | | publisher = Springer |

| series = Lecture Notes in Computer Science |
| series = Lecture Notes in Computer Science |
||

| volume = 11384 |
| volume = 11384 |
||

− | | pages = |
+ | | pages = 105 to 118 |

| abstract = In the context of the challenge of “automatic InterVertebral Disc (IVD) localization and segmentation from 3D multi-modality MR images” that took place at MICCAI 2018, we have proposed a segmentation method based on simple image processing operators. Most of these operators come from the mathematical morphology framework. Driven by some prior knowledge on IVDs (basic information about their shape and the distance between them), and on their contrast in the different modalities, we were able to segment correctly almost every IVD. The most interesting feature of our method is to rely on the morphological structure called the Three of Shapes, which is another way to represent the image contents. This structure arranges all the connected components of an image obtained by thresholding into a tree, where each node represents a particular region. Such structure is actually powerful and versatile for pattern recognition tasks in medical imaging. |
| abstract = In the context of the challenge of “automatic InterVertebral Disc (IVD) localization and segmentation from 3D multi-modality MR images” that took place at MICCAI 2018, we have proposed a segmentation method based on simple image processing operators. Most of these operators come from the mathematical morphology framework. Driven by some prior knowledge on IVDs (basic information about their shape and the distance between them), and on their contrast in the different modalities, we were able to segment correctly almost every IVD. The most interesting feature of our method is to rely on the morphological structure called the Three of Shapes, which is another way to represent the image contents. This structure arranges all the connected components of an image obtained by thresholding into a tree, where each node represents a particular region. Such structure is actually powerful and versatile for pattern recognition tasks in medical imaging. |
||

| lrdepaper = http://www.lrde.epita.fr/dload/papers/carlinet.19.csi.pdf |
| lrdepaper = http://www.lrde.epita.fr/dload/papers/carlinet.19.csi.pdf |
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Imaging (CSI)<nowiki>}</nowiki>, |
Imaging (CSI)<nowiki>}</nowiki>, |
||

year = <nowiki>{</nowiki>2019<nowiki>}</nowiki>, |
year = <nowiki>{</nowiki>2019<nowiki>}</nowiki>, |
||

− | publisher = <nowiki>{</nowiki>Springer |
+ | publisher = <nowiki>{</nowiki>Springer<nowiki>}</nowiki>, |

series = <nowiki>{</nowiki>Lecture Notes in Computer Science<nowiki>}</nowiki>, |
series = <nowiki>{</nowiki>Lecture Notes in Computer Science<nowiki>}</nowiki>, |
||

volume = <nowiki>{</nowiki>11384<nowiki>}</nowiki>, |
volume = <nowiki>{</nowiki>11384<nowiki>}</nowiki>, |
||

− | pages = <nowiki>{</nowiki> |
+ | pages = <nowiki>{</nowiki>105--118<nowiki>}</nowiki>, |

abstract = <nowiki>{</nowiki>In the context of the challenge of ``automatic |
abstract = <nowiki>{</nowiki>In the context of the challenge of ``automatic |
||

InterVertebral Disc (IVD) localization and segmentation |
InterVertebral Disc (IVD) localization and segmentation |

## Latest revision as of 09:51, 19 March 2019

- Authors
- Edwin Carlinet, Thierry Géraud
- Where
- Proceedings of the 5th MICCAI Workshop & Challenge on Computational Methods and Clinical Applications for Spine Imaging (CSI)
- Type
- inproceedings
- Publisher
- Springer
- Projects
- Olena
- Keywords
- Image
- Date
- 2018-11-26

## Contents

## Abstract

In the context of the challenge of “automatic InterVertebral Disc (IVD) localization and segmentation from 3D multi-modality MR images” that took place at MICCAI 2018, we have proposed a segmentation method based on simple image processing operators. Most of these operators come from the mathematical morphology framework. Driven by some prior knowledge on IVDs (basic information about their shape and the distance between them), and on their contrast in the different modalities, we were able to segment correctly almost every IVD. The most interesting feature of our method is to rely on the morphological structure called the Three of Shapes, which is another way to represent the image contents. This structure arranges all the connected components of an image obtained by thresholding into a tree, where each node represents a particular region. Such structure is actually powerful and versatile for pattern recognition tasks in medical imaging.

## Documents

## Source code

Source code available in this archive.

### To build and run the method

tar xf ivdm3seg-lrde.tar.gz mkdir build && cd build cmake ../ivdm3seg-lrde -DCMAKE_BUILD_TYPE=Release cmake --build . --target ivdm3seg

### Usage

Usage: ./ivdm3seg [OPTIONS] FAT.nii INN.nii OPP.nii WAT.nii output.nii

The program uses Pylene, our image processing library.

## Bibtex (lrde.bib)

@InProceedings{ carlinet.19.csi, author = {Edwin Carlinet and Thierry G\'eraud}, title = {Intervertebral Disc Segmentation Using Mathematical Morphology---{A} {CNN}-Free Approach}, booktitle = {Proceedings of the 5th MICCAI Workshop \& Challenge on Computational Methods and Clinical Applications for Spine Imaging (CSI)}, year = {2019}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = {11384}, pages = {105--118}, abstract = {In the context of the challenge of ``automatic InterVertebral Disc (IVD) localization and segmentation from 3D multi-modality MR images'' that took place at MICCAI 2018, we have proposed a segmentation method based on simple image processing operators. Most of these operators come from the mathematical morphology framework. Driven by some prior knowledge on IVDs (basic information about their shape and the distance between them), and on their contrast in the different modalities, we were able to segment correctly almost every IVD. The most interesting feature of our method is to rely on the morphological structure called the Three of Shapes, which is another way to represent the image contents. This structure arranges all the connected components of an image obtained by thresholding into a tree, where each node represents a particular region. Such structure is actually powerful and versatile for pattern recognition tasks in medical imaging.} }