LunaLungCancer / Git / Diff of /README.md

Datasets:

GeorgeSullivan/

LunaLungCancer

Downloads: 1

Data:

De-identified

Image Specialty:

Radiology

Respiratory Medicine Medical Imaging Technique:

CT Medical Imaging Region:

Chest

Lungs Clinical Purpose:

Diagnosis

Screening Task:

Classification

Detection

Segmentation License:

Creative Commons Attribution Non Commercial Share Alike 3.0

Diff of /README.md [72f747] .. [31c399]

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<div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">Luna Lung Cancer</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH nUNNB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><p>The original data page can be found here : <a rel="noreferrer nofollow" aria-label="https://luna16.grand-challenge.org/data/ (opens in a new tab)" target="_blank" href="https://luna16.grand-challenge.org/data/">https://luna16.grand-challenge.org/data/</a></p>
<h2>Data</h2>
<p>For this challenge, we use the publicly available LIDC/IDRI database. This data uses the Creative Commons Attribution 3.0 Unported License. The data for LUNA16 is made available under a similar license, the Creative Commons Attribution 4.0 International License.</p>
<p>We excluded scans with a slice thickness greater than 2.5 mm. In total, 888 CT scans are included. The LIDC/IDRI database also contains annotations which were collected during a two-phase annotation process using 4 experienced radiologists. Each radiologist marked lesions they identified as non-nodule, nodule &lt; 3 mm, and nodules &gt;= 3 mm. See this publication for the details of the annotation process. The reference standard of our challenge consists of all nodules &gt;= 3 mm accepted by at least 3 out of 4 radiologists. Annotations that are not included in the reference standard (non-nodules, nodules &lt; 3 mm, and nodules annotated by only 1 or 2 radiologists) are referred as irrelevant findings. The list of irrelevant findings is provided inside the evaluation script (annotations_excluded.csv).</p>
<p>The full Dataset is provided here : <a rel="noreferrer nofollow" aria-label="https://zenodo.org/record/3723295 (opens in a new tab)" target="_blank" href="https://zenodo.org/record/3723295">https://zenodo.org/record/3723295</a></p>
<h3>The data is structured as follows:</h3>
...
<h3>References</h3>
<p>[1] K. Murphy, B. van Ginneken, A. M. R. Schilham, B. J. de Hoop, H. A. Gietema, and M. Prokop, “A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification,” Medical Image Analysis, vol. 13, pp. 757–770, 2009.</p>
<p>[2] C. Jacobs, E. M. van Rikxoort, T. Twellmann, E. T. Scholten, P. A. de Jong, J. M. Kuhnigk, M. Oudkerk, H. J. de Koning, M. Prokop, C. Schaefer-Prokop, and B. van Ginneken, “Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images,” Medical Image Analysis, vol. 18, pp. 374–384, 2014</p>
<p>[3] A. A. A. Setio, C. Jacobs, J. Gelderblom, and B. van Ginneken, “Automatic detection of large pulmonary solid nodules in thoracic CT images,” Medical Physics, vol. 42, no. 10, pp. 5642–5653, 2015.</p>
<p>[4] E. M. van Rikxoort, B. de Hoop, M. A. Viergever, M. Prokop, and B. van Ginneken, "Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection", Medical Physics, vol. 4236 no. 10, pp. 2934-2947, 2009.</p></div></div></div>

	a/README.md		b/README.md
1	<div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH nUNNB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><p>The original data page can be found here : <a rel="noreferrer nofollow" aria-label="https://luna16.grand-challenge.org/data/ (opens in a new tab)" target="_blank" href="https://luna16.grand-challenge.org/data/">https://luna16.grand-challenge.org/data/</a></p>	1	<div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">Luna Lung Cancer</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH nUNNB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><p>The original data page can be found here : <a rel="noreferrer nofollow" aria-label="https://luna16.grand-challenge.org/data/ (opens in a new tab)" target="_blank" href="https://luna16.grand-challenge.org/data/">https://luna16.grand-challenge.org/data/</a></p>
2	<h2>Data</h2>	2	<h2>Data</h2>
3	<p>For this challenge, we use the publicly available LIDC/IDRI database. This data uses the Creative Commons Attribution 3.0 Unported License. The data for LUNA16 is made available under a similar license, the Creative Commons Attribution 4.0 International License.</p>	3	<p>For this challenge, we use the publicly available LIDC/IDRI database. This data uses the Creative Commons Attribution 3.0 Unported License. The data for LUNA16 is made available under a similar license, the Creative Commons Attribution 4.0 International License.</p>
4	<p>We excluded scans with a slice thickness greater than 2.5 mm. In total, 888 CT scans are included. The LIDC/IDRI database also contains annotations which were collected during a two-phase annotation process using 4 experienced radiologists. Each radiologist marked lesions they identified as non-nodule, nodule < 3 mm, and nodules >= 3 mm. See this publication for the details of the annotation process. The reference standard of our challenge consists of all nodules >= 3 mm accepted by at least 3 out of 4 radiologists. Annotations that are not included in the reference standard (non-nodules, nodules < 3 mm, and nodules annotated by only 1 or 2 radiologists) are referred as irrelevant findings. The list of irrelevant findings is provided inside the evaluation script (annotations_excluded.csv).</p>	4	<p>We excluded scans with a slice thickness greater than 2.5 mm. In total, 888 CT scans are included. The LIDC/IDRI database also contains annotations which were collected during a two-phase annotation process using 4 experienced radiologists. Each radiologist marked lesions they identified as non-nodule, nodule < 3 mm, and nodules >= 3 mm. See this publication for the details of the annotation process. The reference standard of our challenge consists of all nodules >= 3 mm accepted by at least 3 out of 4 radiologists. Annotations that are not included in the reference standard (non-nodules, nodules < 3 mm, and nodules annotated by only 1 or 2 radiologists) are referred as irrelevant findings. The list of irrelevant findings is provided inside the evaluation script (annotations_excluded.csv).</p>
5	<p>The full Dataset is provided here : <a rel="noreferrer nofollow" aria-label="https://zenodo.org/record/3723295 (opens in a new tab)" target="_blank" href="https://zenodo.org/record/3723295">https://zenodo.org/record/3723295</a></p>	5	<p>The full Dataset is provided here : <a rel="noreferrer nofollow" aria-label="https://zenodo.org/record/3723295 (opens in a new tab)" target="_blank" href="https://zenodo.org/record/3723295">https://zenodo.org/record/3723295</a></p>
6	<h3>The data is structured as follows:</h3>	6	<h3>The data is structured as follows:</h3>
	...		...
29	<h3>References</h3>	29	<h3>References</h3>
30	<p>[1] K. Murphy, B. van Ginneken, A. M. R. Schilham, B. J. de Hoop, H. A. Gietema, and M. Prokop, “A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification,” Medical Image Analysis, vol. 13, pp. 757–770, 2009.</p>	30	<p>[1] K. Murphy, B. van Ginneken, A. M. R. Schilham, B. J. de Hoop, H. A. Gietema, and M. Prokop, “A large scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification,” Medical Image Analysis, vol. 13, pp. 757–770, 2009.</p>
31	<p>[2] C. Jacobs, E. M. van Rikxoort, T. Twellmann, E. T. Scholten, P. A. de Jong, J. M. Kuhnigk, M. Oudkerk, H. J. de Koning, M. Prokop, C. Schaefer-Prokop, and B. van Ginneken, “Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images,” Medical Image Analysis, vol. 18, pp. 374–384, 2014</p>	31	<p>[2] C. Jacobs, E. M. van Rikxoort, T. Twellmann, E. T. Scholten, P. A. de Jong, J. M. Kuhnigk, M. Oudkerk, H. J. de Koning, M. Prokop, C. Schaefer-Prokop, and B. van Ginneken, “Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images,” Medical Image Analysis, vol. 18, pp. 374–384, 2014</p>
32	<p>[3] A. A. A. Setio, C. Jacobs, J. Gelderblom, and B. van Ginneken, “Automatic detection of large pulmonary solid nodules in thoracic CT images,” Medical Physics, vol. 42, no. 10, pp. 5642–5653, 2015.</p>	32	<p>[3] A. A. A. Setio, C. Jacobs, J. Gelderblom, and B. van Ginneken, “Automatic detection of large pulmonary solid nodules in thoracic CT images,” Medical Physics, vol. 42, no. 10, pp. 5642–5653, 2015.</p>
33	<p>[4] E. M. van Rikxoort, B. de Hoop, M. A. Viergever, M. Prokop, and B. van Ginneken, "Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection", Medical Physics, vol. 4236 no. 10, pp. 2934-2947, 2009.</p></div></div></div>	33	<p>[4] E. M. van Rikxoort, B. de Hoop, M. A. Viergever, M. Prokop, and B. van Ginneken, "Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection", Medical Physics, vol. 4236 no. 10, pp. 2934-2947, 2009.</p></div></div></div>