The layered surface tool uses a sparse layered graphs in order detect layers in 2D and 3D images. The primary benefit of using this tool over standard edge detection is that it can detect layers even when there edge breaks or gaps in the layer.
The layered surface tool uses a sparse layered graphs in order detect layers in 2D and 3D images. The primary benefit of using this tool over standard edge detection is that it can detect layers even when there are edge breaks or gaps in the layer.
This repository contains tutorials for the using the layered surface detection tool. Included is a few Jupyter notebook tutorials that are designed to give you an idea on how to use the layered surface tool. You should start with the _LayeredSurfaceDetection_tutorial.ipynb_ notebook which discuss the basics of the layered surface tool and applies it to some synthetic data as well as a relatively simple example dataset. More complex examples are provided in the _NerveSegmentation2D_example.ipynb_ and _NerveSegmentation3D_example.ipynb_ notebooks which describe how to apply the layered surface tool to circular regions via radial unwrapping. A Python file containing some helper functions that are used in the tutorials is also included in utilsLS.py_. Some visualization functions are included in the _utilsVisualizationLS.py_.
This repository contains tutorials for using the layered surface detection tool. Included is a few Jupyter notebook tutorials that are designed to give you an idea on how to use the layered surface tool. You should start with the [LayeredSurfaceDetection_tutorial.ipynb](LayeredSurfaceDetection_tutorial.ipynb) notebook which discuss the basics of the layered surface tool and applies it on synthetic data as well as on simple real-world data. More complex examples are provided in the [NerveSegmentation2D_example.ipynb](NerveSegmentation2D_example.ipynb) and [NerveSegmentation3D_example.ipynb](NerveSegmentation3D_example.ipynb) notebooks, which describe how to apply the layered surface tool to circular regions via radial unwrapping. A Python file containing helper functions used in the tutorials is included in [utilsLS.py](utilsLS.py), and visualization functions are included in the [utilsVisualizationLS.py](utilsVisualizationLS.py).
These tutorials will give you an understanding of how to apply the Layered Surface tool to image data so that you can use the tool on your own datasets. You can open the the tutorials at [](https://mybinder.org/v2/git/https%3A%2F%2Flab.compute.dtu.dk%2FQIM%2Ftutorials%2Flayered-surfaces/HEAD).
These tutorials will give you an understanding of how to apply the Layered Surface tool to image data so that you can use the tool on your own data. You can open the tutorials at [](https://mybinder.org/v2/git/https%3A%2F%2Flab.compute.dtu.dk%2FQIM%2Ftutorials%2Flayered-surfaces/HEAD).
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Please contact William Laprade wl@di.ku.dk with issues and feedback.
If you use the tool for research, please cite the developers' original paper: [Sparse layered graphs for multi-object segmentation.](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jeppesen_Sparse_Layered_Graphs_for_Multi-Object_Segmentation_CVPR_2020_paper.pdf).
If you use the tool for research, please cite the original paper: [Sparse layered graphs for multi-object segmentation.](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jeppesen_Sparse_Layered_Graphs_for_Multi-Object_Segmentation_CVPR_2020_paper.pdf).
```
@inproceedings{jeppesen2020sparse,
title={Sparse Layered Graphs for Multi-Object Segmentation},
author={Jeppesen, Niels and Christensen, Anders N and Dahl, Vedrana A and Dahl, Anders B},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
