Documentation/Nightly/Modules/DeepInfer - Revision history

Mehrtash at 00:36, 12 August 2017

2017-08-12T00:36:16Z

2017-06-05T16:42:57Z

2017-06-05T16:42:16Z

2017-04-11T22:57:17Z

2017-04-11T22:52:31Z

2017-04-10T23:23:56Z

Packaging and Deployment Example

2017-04-10T23:16:50Z

2017-04-10T23:13:07Z

2017-04-10T23:08:31Z

2017-04-10T22:49:25Z

@@ Line 36: / Line 36: @@
 Docker for Windows requires Windows 10 Pro (64bit) and Microsoft Hyper-V. You also need to enable virtualization in BIOS settings.
 * [https://docs.docker.com/docker-for-windows/install/ Docker installation guide for Windows]
-* Share drive C with docker containers from settings.
+* VERY IMPORTANT: Share drive C with docker containers from settings.
 ==== Mac OS X ====

@@ Line 26: / Line 26: @@
 [[File:Deepinfer-256.png|128px|]]
 <br>
-DeepInfer is an open-source deep learning deployment toolkit for medical imaging and Image-Guided Therapy. With DeepInfer 3D Slicer users can deploy trained
+DeepInfer is an open-source deep learning deployment toolkit for medical imaging and image-guided therapy. With DeepInfer 3D Slicer users can deploy trained
 deep learning and machine learning models in their workflow. Also,  DeepInfer allows machine learning experts to package,
 ship, and deploy their models, thereby additionally allowing clinical researchers and biomedical engineers to use

@@ Line 26: / Line 26: @@
 [[File:Deepinfer-256.png|128px|]]
 <br>
-DeepInfer is an Open-Source Deep Learning Deployment Toolkit for medical imaging and Image-Guided Therapy. With DeepInfer 3D Slicer users can deploy trained
+DeepInfer is an open-source deep learning deployment toolkit for medical imaging and Image-Guided Therapy. With DeepInfer 3D Slicer users can deploy trained
 deep learning and machine learning models in their workflow. Also,  DeepInfer allows machine learning experts to package,
 ship, and deploy their models, thereby additionally allowing clinical researchers and biomedical engineers to use

@@ Line 50: / Line 50: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Panels and their use}}
+[[File:Deepinfer-panels.png|600px|]]
 {{documentation/{{documentation/version}}/module-section|Deployed Models}}

@@ Line 45: / Line 45: @@
 * Give non-root access to docker using this [http://askubuntu.com/a/477554 guide].
-{{documentation/{{documentation/version}}/module-section|Tutorials}}
+{{documentation/{{documentation/version}}/module-section|Demo}}
 <!-- ---------------------------- -->

@@ Line 79: / Line 79: @@
 ==== Packaging and Deployment Example ====
+* [https://github.com/mehrtash/Prostate-Segmenter/blob/master/docker/Dockerfile.cpu Example of a CPU DockerFile]
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@@ Line 67: / Line 67: @@
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Information for Developers}}
-Figure below shows the architecture of the DeepInfer toolkit, which has three components: the Docker engine,
+==== Architecture ====
 the DeepInfer 3D Slicer extension [1], and the cloud model registry [2].
 The Docker engine consists of local Docker containers that include the deployed models, as well as all of the required deep learning frameworks, which
 process incoming data and produce respective results.
-Data streaming between the Docker engine and the GUI is achieved by sharing a local folder with the specific Docker image.
+Data streaming between the Docker engine and the GUI is achieved by sharing a local folder with the specific Docker image.<br><br>
+[[File:Deepifner-arch.png|800px|]]<br><br>
 #  [https://github.com/deepinfer/Slicer-DeepInfer Slicer-DeepInfer Module Source Code]
 #  [https://github.com/DeepInfer/Model-Registry Model Registry]
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← Older revision		Revision as of 23:08, 10 April 2017
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	{{documentation/{{documentation/version}}/module-section\|Deployed Models}}		{{documentation/{{documentation/version}}/module-section\|Deployed Models}}
		+	==== Prostate Segmentation in Targeted MRI-Guided Biopsy ====
		+	This model is trained for the task of rough delineation of the prostate gland that is a necessary step in MRI-guided targeted biopsy. The deployed model is trained on transaxial T2-weighted MRIs of the prostate (without endorectal coil on a 3T MRI machine) collected during MR-guided prostate biopsy procedures. The network based on a customized variant of the U-Net architecture. The network is trained on N = 224 patients on a total number of 26250 2D slices of prostate images and achieved 76.25% accuracy on N = 57 validation patients (2184 2D slices).<br><br>

		+	[[File:Prostate-deepinfer.png\|800px\|]]<br><br>

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