From cbc97b6921d2f89c89e0950b1fc3a60817643d71 Mon Sep 17 00:00:00 2001 From: Peter Hessey Date: Wed, 2 Nov 2022 12:08:40 +0000 Subject: [PATCH] =?UTF-8?q?=F0=9F=93=9D=20Fix=20doc=20typo?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/source/md/sample_tasks.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/md/sample_tasks.md b/docs/source/md/sample_tasks.md index 34b09a293..feae8edb2 100644 --- a/docs/source/md/sample_tasks.md +++ b/docs/source/md/sample_tasks.md @@ -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_) [[1]](#1). +The dataset is available [here](https://zenodo.org/record/1481223#.Xs-ehzPiuM_) [1]. 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. @@ -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) [[2]](#2). +This example is based on the [Lung CT Segmentation Challenge 2017](https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017) [2]. ### Downloading and preparing the lung dataset -The dataset [[3]][#3]([4](#4) can be downloaded [here](https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017#021ca3c9a0724b0d9df784f1699d35e2). +The dataset [3][4] 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. @@ -109,17 +109,17 @@ See [Model Training](building_models.md) for details on training outputs, resumi ### References -[1] +[1] Ishikawa, Hiroshi. (2018). OCT volumes for glaucoma detection (Version 1.0.0) [Data set]. Zenodo. -[2] +[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) -[3] +[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. -[4] +[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))