UniToChest / Git / Diff of /README.md

Datasets:

GeorgeSullivan/

UniToChest

Downloads: 1

Data:

Image Specialty:

Oncology

Radiology Medical Imaging Technique:

CT Medical Imaging Region:

Chest

Lungs Clinical Purpose:

Diagnosis Task:

Detection

Segmentation License:

Unknown

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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">Uni To Chest</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH flNyFK"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><p>This dataset is a preprocessed version of the original UniToChest dataset. The UniToChest dataset, created by Chaudhry et al. and made available through their research work, serves as the foundation for the following preprocessing techniques applied here:</p>
<ol>
<li><p><strong>Hounsfield Units</strong>: The raw CT scan values have been converted into Hounsfield Units (HU). </p></li>
<li><p><strong>Windowing</strong>: The dataset includes images with windowing applied, a technique commonly used to enhance specific ranges of Hounsfield Units, thereby improving the visualization of certain tissues.</p></li>
<li><p><strong>Lung Segmentation:</strong> This preprocessing step isolates the lung regions within the CT scans, using U-Net R231 and thresholding, allowing for focused analysis of lung tissue.</p></li>
<li><p><strong>CLAHE (Contrast Limited Adaptive Histogram Equalization)</strong>: CLAHE has been applied to improve the contrast of the images, particularly in areas with low contrast.</p></li>
...
</ul>
<p><strong>References</strong></p>
<p>[1] Daniele Perlo, «UniToChest». Zenodo, dic. 22, 2021. doi: 10.5281/zenodo.5797912.<br>
[2] H. A. H. Chaudhry et&nbsp;al., «UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans», en Image Analysis and Processing – ICIAP 2022, S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, y F. Tombari, Eds., Cham: Springer International Publishing, 2022, pp. 185-196. doi: 10.1007/978-3-031-06427-2_16.<br>
[3] J. Hofmanninger, F. Prayer, J. Pan, et al., "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem," Eur Radiol Exp, vol. 4, p. 50, 2020. doi: 10.1186/s41747-020-00173-2.</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 flNyFK"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><p>This dataset is a preprocessed version of the original UniToChest dataset. The UniToChest dataset, created by Chaudhry et al. and made available through their research work, serves as the foundation for the following preprocessing techniques applied here:</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">Uni To Chest</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH flNyFK"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><p>This dataset is a preprocessed version of the original UniToChest dataset. The UniToChest dataset, created by Chaudhry et al. and made available through their research work, serves as the foundation for the following preprocessing techniques applied here:</p>
2	<ol>	2	<ol>
3	<li><p><strong>Hounsfield Units</strong>: The raw CT scan values have been converted into Hounsfield Units (HU). </p></li>	3	<li><p><strong>Hounsfield Units</strong>: The raw CT scan values have been converted into Hounsfield Units (HU). </p></li>
4	<li><p><strong>Windowing</strong>: The dataset includes images with windowing applied, a technique commonly used to enhance specific ranges of Hounsfield Units, thereby improving the visualization of certain tissues.</p></li>	4	<li><p><strong>Windowing</strong>: The dataset includes images with windowing applied, a technique commonly used to enhance specific ranges of Hounsfield Units, thereby improving the visualization of certain tissues.</p></li>
5	<li><p><strong>Lung Segmentation:</strong> This preprocessing step isolates the lung regions within the CT scans, using U-Net R231 and thresholding, allowing for focused analysis of lung tissue.</p></li>	5	<li><p><strong>Lung Segmentation:</strong> This preprocessing step isolates the lung regions within the CT scans, using U-Net R231 and thresholding, allowing for focused analysis of lung tissue.</p></li>
6	<li><p><strong>CLAHE (Contrast Limited Adaptive Histogram Equalization)</strong>: CLAHE has been applied to improve the contrast of the images, particularly in areas with low contrast.</p></li>	6	<li><p><strong>CLAHE (Contrast Limited Adaptive Histogram Equalization)</strong>: CLAHE has been applied to improve the contrast of the images, particularly in areas with low contrast.</p></li>
	...		...
20	</ul>	20	</ul>
21	<p><strong>References</strong></p>	21	<p><strong>References</strong></p>
22	<p>[1] Daniele Perlo, «UniToChest». Zenodo, dic. 22, 2021. doi: 10.5281/zenodo.5797912.<br>	22	<p>[1] Daniele Perlo, «UniToChest». Zenodo, dic. 22, 2021. doi: 10.5281/zenodo.5797912.<br>
23	[2] H. A. H. Chaudhry et al., «UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans», en Image Analysis and Processing – ICIAP 2022, S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, y F. Tombari, Eds., Cham: Springer International Publishing, 2022, pp. 185-196. doi: 10.1007/978-3-031-06427-2_16.<br>	23	[2] H. A. H. Chaudhry et al., «UniToChest: A Lung Image Dataset for Segmentation of Cancerous Nodules on CT Scans», en Image Analysis and Processing – ICIAP 2022, S. Sclaroff, C. Distante, M. Leo, G. M. Farinella, y F. Tombari, Eds., Cham: Springer International Publishing, 2022, pp. 185-196. doi: 10.1007/978-3-031-06427-2_16.<br>
24	[3] J. Hofmanninger, F. Prayer, J. Pan, et al., "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem," Eur Radiol Exp, vol. 4, p. 50, 2020. doi: 10.1186/s41747-020-00173-2.</p></div></div></div>	24	[3] J. Hofmanninger, F. Prayer, J. Pan, et al., "Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem," Eur Radiol Exp, vol. 4, p. 50, 2020. doi: 10.1186/s41747-020-00173-2.</p></div></div></div>