https://www.slicer.org/w/api.php?action=feedcontributions&feedformat=atom&user=Inesmunoz 2026-04-30T22:06:55Z User contributions MediaWiki 1.33.0 https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61472 2019-09-22T10:05:09Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> &lt;gallery&gt;<br /> segmentationresults.jpg<br /> &lt;/gallery&gt;<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Once installed, the Docker Image that stores the Neural Network should be pulled from the repository by typing the following code in the Terminal of your computer: &lt;br /&gt;<br /> $docker pull inesmunoz/hn-segmenter<br /> <br /> The pull will take few minutes to download, and it takes 2.69GB of memory. &lt;br /&gt;<br /> &lt;br /&gt;<br /> Toc check if the Image was downloaded correctly one can type:&lt;br /&gt;<br /> $docker images<br /> <br /> And find the image there.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Now, the module is ready to execute the communications with Docker to obtain segmentations.<br /> <br /> == Panels and their use ==<br /> The user interface is composed by only 3 sections, according to the three main functions of the module: loading an image, obtaining segmentations and saving the new volume. &lt;br /&gt;<br /> This is possible thanks to the segmentation button, which performs few hidden functions. This button managed to automate the conversion of DICOM to nifty format, the importation of labelmaps into segmentations and all Terminal calls to execute Docker.&lt;br /&gt;<br /> &lt;br /&gt;<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.&lt;br /&gt;&lt;br /&gt;<br /> &lt;br /&gt;<br /> The module proved to be consistent and robust obtaining segmentations for all cases except for those images that were cut. Segmentations are usually smooth in shape and the failures are easy to detect and correct. &lt;br /&gt;<br /> &lt;br /&gt;<br /> &lt;br /&gt;<br /> <br /> [[File:segmentationresults.jpg]]<br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=File:Segmentationresults.jpg&diff=61471 2019-09-22T10:03:14Z

<p>Inesmunoz: Inesmunoz uploaded a new version of File:Segmentationresults.jpg</p> <hr /> <div></div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61470 2019-09-22T10:01:18Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> &lt;gallery&gt;<br /> segmentationresults.jpg<br /> &lt;/gallery&gt;<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Once installed, the Docker Image that stores the Neural Network should be pulled from the repository by typing the following code in the Terminal of your computer: &lt;br /&gt;<br /> $docker pull inesmunoz/hn-segmenter<br /> <br /> The pull will take few minutes to download, and it takes 2.69GB of memory. &lt;br /&gt;<br /> &lt;br /&gt;<br /> Toc check if the Image was downloaded correctly one can type:&lt;br /&gt;<br /> $docker images<br /> <br /> And find the image there.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Now, the module is ready to execute the communications with Docker to obtain segmentations.<br /> <br /> == Panels and their use ==<br /> The user interface is composed by only 3 sections, according to the three main functions of the module: loading an image, obtaining segmentations and saving the new volume. &lt;br /&gt;<br /> This is possible thanks to the segmentation button, which performs few hidden functions. This button managed to automate the conversion of DICOM to nifty format, the importation of labelmaps into segmentations and all Terminal calls to execute Docker.<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.&lt;br /&gt;&lt;br /&gt;<br /> &lt;br /&gt;<br /> The module proved to be consistent and robust obtaining segmentations for all cases except for those images that were cut. Segmentations are usually smooth in shape and the failures are easy to detect and correct. &lt;br /&gt;<br /> &lt;br /&gt;<br /> &lt;br /&gt;<br /> <br /> [[File:segmentationresults.jpg]]<br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61469 2019-09-22T09:51:38Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Once installed, the Docker Image that stores the Neural Network should be pulled from the repository by typing the following code in the Terminal of your computer: &lt;br /&gt;<br /> $docker pull inesmunoz/hn-segmenter<br /> <br /> The pull will take few minutes to download, and it takes 2.69GB of memory. &lt;br /&gt;<br /> &lt;br /&gt;<br /> Toc check if the Image was downloaded correctly one can type:&lt;br /&gt;<br /> $docker images<br /> <br /> And find the image there.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Now, the module is ready to execute the communications with Docker to obtain segmentations.<br /> <br /> == Panels and their use ==<br /> The user interface is composed by only 3 sections, according to the three main functions of the module: loading an image, obtaining segmentations and saving the new volume. &lt;br /&gt;<br /> This is possible thanks to the segmentation button, which performs few hidden functions. This button managed to automate the conversion of DICOM to nifty format, the importation of labelmaps into segmentations and all Terminal calls to execute Docker.<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> [[File:segmentationresults.jpg]]<br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61468 2019-09-22T09:50:21Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Once installed, the Docker Image that stores the Neural Network should be pulled from the repository by typing the following code in the Terminal of your computer: &lt;br /&gt;<br /> $docker pull inesmunoz/hn-segmenter<br /> <br /> The pull will take few minutes to download, and it takes 2.69GB of memory. &lt;br /&gt;<br /> &lt;br /&gt;<br /> Toc check if the Image was downloaded correctly one can type:&lt;br /&gt;<br /> $docker images<br /> <br /> And find the image there.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Now, the module is ready to execute the communications with Docker to obtain segmentations.<br /> <br /> == Panels and their use ==<br /> The user interface is composed by only 3 sections, according to the three main functions of the module: loading an image, obtaining segmentations and saving the new volume. &lt;br /&gt;<br /> This is possible thanks to the segmentation button, which performs few hidden functions. This button managed to automate the conversion of DICOM to nifty format, the importation of labelmaps into segmentations and all Terminal calls to execute Docker.<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> &lt;gallery&gt;<br /> segmentationresults.jpg<br /> &lt;/gallery&gt;<br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61467 2019-09-22T09:49:00Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Once installed, the Docker Image that stores the Neural Network should be pulled from the repository by typing the following code in the Terminal of your computer: &lt;br /&gt;<br /> $docker pull inesmunoz/hn-segmenter<br /> <br /> The pull will take few minutes to download, and it takes 2.69GB of memory. &lt;br /&gt;<br /> &lt;br /&gt;<br /> Toc check if the Image was downloaded correctly one can type:&lt;br /&gt;<br /> $docker images<br /> <br /> And find the image there.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Now, the module is ready to execute the communications with Docker to obtain segmentations.<br /> <br /> == Panels and their use ==<br /> The user interface is composed by only 3 sections, according to the three main functions of the module: loading an image, obtaining segmentations and saving the new volume. &lt;br /&gt;<br /> This is possible thanks to the segmentation button, which performs few hidden functions. This button managed to automate the conversion of DICOM to nifty format, the importation of labelmaps into segmentations and all Terminal calls to execute Docker.<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=File:Segmentationresults.jpg&diff=61466 2019-09-22T09:44:24Z

<p>Inesmunoz: </p> <hr /> <div></div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61465 2019-09-22T09:25:14Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Once installed, the Docker Image that stores the Neural Network should be pulled from the repository by typing the following code in the Terminal of your computer: &lt;br /&gt;<br /> $docker pull inesmunoz/hn-segmenter<br /> <br /> The pull will take few minutes to download, and it takes 2.69GB of memory. &lt;br /&gt;<br /> &lt;br /&gt;<br /> Toc check if the Image was downloaded correctly one can type:&lt;br /&gt;<br /> $docker images<br /> <br /> And find the image there.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Now, the module is ready to execute the communications with Docker to obtain segmentations.<br /> <br /> == Panels and their use ==<br /> The user interface is composed by only 3 sections, according to the three main functions of the module: loading an image, obtaining segmentations and saving the new volume. &lt;br /&gt;<br /> This is possible thanks to the segmentation button, which performs few hidden functions. This button managed to automate the conversion of DICOM to nifty format, the importation of labelmaps into segmentations and all Terminal calls to execute Docker.<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61464 2019-09-22T09:23:16Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Once installed, the Docker Image that stores the Neural Network should be pulled from the repository by typing the following code in the Terminal of your computer: &lt;br /&gt;<br /> $docker pull inesmunoz/hn-segmenter<br /> <br /> The pull will take few minutes to download, and it takes 2.69GB of memory. &lt;br /&gt;<br /> &lt;br /&gt;<br /> Toc check if the Image was downloaded correctly one can type:&lt;br /&gt;<br /> $docker images<br /> <br /> And find the image there.&lt;br /&gt;<br /> &lt;br /&gt;<br /> Now, the module is ready to execute the communications with Docker to obtain segmentations.<br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61463 2019-09-22T09:13:06Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> &lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61462 2019-09-22T09:12:48Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.&lt;br /&gt;<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61461 2019-09-22T09:12:26Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARs) segmentations: left and right eye, left and right parotid, brainstem and spinal cord.<br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy by reducing planning time with automatic segmentation tools, instead of manual segmentations.<br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61437 2019-09-14T11:36:19Z

<p>Inesmunoz: Blanked the page</p> <hr /> <div></div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/SlicerHNSegmenter&diff=61436 2019-09-14T11:36:09Z

<p>Inesmunoz: Created page with &quot;== Module Description == HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect...&quot;</p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61425 2019-09-12T20:27:18Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61424 2019-09-12T20:15:13Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link.<br /> <br /> [[File:Segmentation-results.jpg]]<br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61423 2019-09-12T20:05:53Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels-hn-segmenter.jpg]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link. <br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=File:Panels-hn-segmenter.jpg&diff=61422 2019-09-12T20:00:22Z

<p>Inesmunoz: </p> <hr /> <div></div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61421 2019-09-12T19:57:20Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels buttons of HN Segmenter module.png]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link. <br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61420 2019-09-12T19:56:01Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels buttons of HN Segmenter module.png|thumb|Panels buttons of HN Segmenter module]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link. <br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61419 2019-09-12T19:54:11Z

<p>Inesmunoz: </p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels buttons of HN Segmenter module.png|Panels buttons of HN Segmenter module]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link. <br /> <br /> <br /> == Relevant Links ==<br /> [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=Documentation/Nightly/Extensions/HN_Segmenter_Module&diff=61418 2019-09-12T19:51:28Z

<p>Inesmunoz: Created page with &quot;== Module Description == HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect...&quot;</p> <hr /> <div>== Module Description ==<br /> HN Segmenter module is a 3D Slicer module intended to provide segmentations to a given CT head and neck image. It provides a user interface to connect with Docker and obtain 6 organs-at-risk (OARa) segmentations. <br /> It constitutes a part of a project designed to accelerate the treatment planning in Radiotherapy. <br /> <br /> <br /> == Setup Guide ==<br /> In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br /> <br /> <br /> == Panels and their use ==<br /> <br /> [[File:Panels buttons of HN Segmenter module.png|Panels buttons of HN Segmenter module]]<br /> <br /> <br /> == Deployed model ==<br /> The dataset used to test and evaluate the performance of the module is composed by a set of CT scans obtained from the Structure Segmentation for Radiotherapy Planning Challenge 2019, a competition of MICCAI 2019 Challenge. Find [https://structseg2019.grand-challenge.org/Home/ here] the link. <br /> <br /> <br /> == Relevant Links ==<br /> [[Slicer-HN-Segmenter Module source code|https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master]]</div>

Inesmunoz https://www.slicer.org/w/index.php?title=User:Inesmunoz&diff=61414 2019-09-11T16:48:26Z

<p>Inesmunoz: </p> <hr /> <div>Biomedical Engineering student at Universidad Politécnica de Valencia(sept2015 oct2019). Studied six months at Universidad Carlos III de Madrid. <br /> Currently working on her thesis on &quot;Design of an Automatic OAR Segmentation Tool for Head and Neck cancers&quot; based on Slicer and Docker. <br /> Working as a Quality Systems Trainee at Edwards Lifesciences since July 1st 2019.</div>

Inesmunoz