Skip to content
This repository was archived by the owner on Mar 21, 2024. It is now read-only.

DOC: Fix doc typo #820

Merged
merged 1 commit into from
Nov 2, 2022
Merged

DOC: Fix doc typo #820

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 7 additions & 7 deletions docs/source/md/sample_tasks.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@ This example is based on the paper [A feature agnostic approach for glaucoma det

### Downloading and preparing the glaucoma dataset

The dataset is available [here](https://zenodo.org/record/1481223#.Xs-ehzPiuM_) <sup>[[1]](#1)</sup>.
The dataset is available [here](https://zenodo.org/record/1481223#.Xs-ehzPiuM_) <sup>[1]</sup>.

After downloading and extracting the zip file, run the [create_glaucoma_dataset_csv.py](https://github.com/microsoft/InnerEye-DeepLearning/blob/main/InnerEye/Scripts/create_glaucoma_dataset_csv.py)
script on the extracted folder.
Expand Down Expand Up @@ -54,11 +54,11 @@ Please check [here](innereye_as_submodule.md) for details.

## Sample segmentation task: Segmentation of Lung CT

This example is based on the [Lung CT Segmentation Challenge 2017](https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017) <sup>[[2]](#2)</sup>.
This example is based on the [Lung CT Segmentation Challenge 2017](https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017) <sup>[2]</sup>.

### Downloading and preparing the lung dataset

The dataset <sup>[[3]][#3]([4](#4)</sup> can be downloaded [here](https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017#021ca3c9a0724b0d9df784f1699d35e2).
The dataset <sup>[3][4]</sup> can be downloaded [here](https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017#021ca3c9a0724b0d9df784f1699d35e2).

You need to convert the dataset from DICOM-RT to NIFTI. Before this, place the downloaded dataset in another
parent folder, which we will call `datasets`. This file structure is expected by the conversion tool.
Expand Down Expand Up @@ -109,17 +109,17 @@ See [Model Training](building_models.md) for details on training outputs, resumi

### References

<a id="1">[1]</a>
[1]
Ishikawa, Hiroshi. (2018). OCT volumes for glaucoma detection (Version 1.0.0) [Data set]. Zenodo. <http://doi.org/10.5281/zenodo.1481223>

<a id="2">[2]</a>
[2]
Yang, J. , Veeraraghavan, H. , Armato, S. G., Farahani, K. , Kirby, J. S., Kalpathy-Kramer, J. , van Elmpt, W. , Dekker, A. , Han, X. , Feng, X. , Aljabar, P. , Oliveira, B. , van der Heyden, B. , Zamdborg, L. , Lam, D. , Gooding, M. and Sharp, G. C. (2018),
Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med. Phys.. . [doi:10.1002/mp.13141](https://doi.org/10.1002/mp.13141)

<a id="3">[3]</a>
[3]
Yang, Jinzhong; Sharp, Greg; Veeraraghavan, Harini ; van Elmpt, Wouter ; Dekker, Andre; Lustberg, Tim; Gooding, Mark. (2017).
Data from Lung CT Segmentation Challenge. The Cancer Imaging Archive. <http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08>

<a id="4">[4]</a>
[4]
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F.
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. ([paper](http://link.springer.com/article/10.1007%2Fs10278-013-9622-7))