Documentation/Nightly/Extensions/SlicerHNSegmenter - Revision history

Inesmunoz at 10:05, 22 September 2019

2019-09-22T10:05:09Z

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 == Panels and their use ==
 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. <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.
+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 />
 [[File:Panels-hn-segmenter.jpg]]

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 <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.
 == Setup Guide ==
@@ Line 26: / Line 29: @@
 == Deployed model ==
-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.
+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:segmentationresults.jpg]]
 == Relevant Links ==
 [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]

@@ Line 27: / Line 27: @@
 == Deployed model ==
 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.
-<gallery>
+[[File:segmentationresults.jpg]]
 == Relevant Links ==
 [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]

@@ Line 27: / Line 27: @@
 == Deployed model ==
 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.
 == Relevant Links ==
 [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]

@@ Line 27: / Line 27: @@
 == Deployed model ==
 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.
 == Relevant Links ==
 [https://github.com/inesmunozz/Slicer-HN_Segmenter/tree/master Slicer HN-Segmenter Module Source Code]

@@ Line 5: / Line 5: @@
 == Setup Guide ==
-In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.
+In order to use HN Segmenter, [https://www.docker.com Docker] is required to be installed and configured properly.<br />
 == Panels and their use ==

@@ Line 1: / Line 1: @@
 == 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 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.