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Fix typo using "codespell" python package

This commit was crafted by:

(1) installing codespell

(2) running this one-liner

  for dir in $(ls -1 | grep PW); do (cd $dir; codespell -w); done

(3) reviewing changes and discarding incorrect fixes
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jcfr committed Jun 24, 2019
1 parent a43fd1b commit c89afe3e4b9e2ee75c74108e60e00b1a5cbd96ff
Showing with 96 additions and 96 deletions.
  1. +1 −1 PW27_2018_Boston/BreakoutSessions/AR-VR.md
  2. +2 −2 PW27_2018_Boston/BreakoutSessions/FDA-and-3D-Slicer.md
  3. +1 −1 PW27_2018_Boston/PreparatoryMeetingsNotes.md
  4. +1 −1 PW27_2018_Boston/Projects/CIPDeepLearningLungSegmentation/README.md
  5. +1 −1 PW27_2018_Boston/Projects/CompressedVideoSaving/README.md
  6. +1 −1 PW27_2018_Boston/Projects/ESLD_DSS/README.md
  7. +1 −1 PW27_2018_Boston/Projects/ExtensionsWithCUDA/README.md
  8. +1 −1 PW27_2018_Boston/Projects/FiberClusteringAtlas/README.md
  9. +2 −2 PW27_2018_Boston/Projects/GirderWebCloud/README.md
  10. +3 −3 PW27_2018_Boston/Projects/MedicalInfraredImagingwithSlicer/README.md
  11. +1 −1 PW27_2018_Boston/Projects/ModelFittingTools/README.md
  12. +4 −4 PW27_2018_Boston/Projects/OrganmotionCompensationInMR/README.md
  13. +1 −1 PW27_2018_Boston/Projects/ProstateMpMRIWebViewer/README.md
  14. +1 −1 PW27_2018_Boston/Projects/QuantitativeSmallAnimalImaging/README.md
  15. +1 −1 PW27_2018_Boston/Projects/RadiomicsRepeatability/README.md
  16. +1 −1 PW27_2018_Boston/Projects/SlicerGuidedUltraSoundCalibration/README.md
  17. +1 −1 PW27_2018_Boston/Projects/SlicerSALT/README.md
  18. +1 −1 PW28_2018_GranCanaria/Breakouts/TutorialsReview/README.md
  19. +3 −3 PW28_2018_GranCanaria/Projects/3DViewsLinking/README.md
  20. +7 −7 PW28_2018_GranCanaria/Projects/4D_MRI_via_retrospectiv_stacking/README.md
  21. +3 −3 PW28_2018_GranCanaria/Projects/AdaptiveMIS/README.md
  22. +1 −1 PW28_2018_GranCanaria/Projects/CIP_Python3/README.md
  23. +1 −1 PW28_2018_GranCanaria/Projects/CustomGUIForUSSimulator/Readme.md
  24. +1 −1 PW28_2018_GranCanaria/Projects/DICOMweb/README.md
  25. +4 −4 PW28_2018_GranCanaria/Projects/EvaluationOfProjects/README.md
  26. +3 −3 PW28_2018_GranCanaria/Projects/FWFintegration/README.md
  27. +1 −1 PW28_2018_GranCanaria/Projects/Insula_segmentation_with_3DSlicer/README.md
  28. +2 −2 PW28_2018_GranCanaria/Projects/MultiVolumeRendering/README.md
  29. +1 −1 PW28_2018_GranCanaria/Projects/NeedleSegmentationDeployment/README.md
  30. +3 −3 PW28_2018_GranCanaria/Projects/STIM_ICM_Project/README.md
  31. +1 −1 PW28_2018_GranCanaria/Projects/SegmentationGeometryWidget/README.md
  32. +1 −1 PW28_2018_GranCanaria/Projects/Slicer5Roadmap/README.md
  33. +1 −1 PW29_2018_London_Canada/Projects/BrainVentricleSegment/README.md
  34. +1 −1 PW29_2018_London_Canada/Projects/Learning3DSlicer/README.md
  35. +1 −1 PW29_2018_London_Canada/Projects/Scanner Remote Control/README.md
  36. +1 −1 PW29_2018_London_Canada/Projects/SlicerCustomApp/README.md
  37. +1 −1 PW29_2018_London_Canada/Projects/TEECalibration/README.md
  38. +3 −3 PW29_2018_London_Canada/README.md
  39. +1 −1 PW30_2019_GranCanaria/BreakoutSessions/MachineLearning.md
  40. +1 −1 PW30_2019_GranCanaria/Projects/AutomSegmentFreeSurfer/README.md
  41. +1 −1 PW30_2019_GranCanaria/Projects/DICOMSEG-Cornerstone-VTKJS/README.md
  42. +1 −1 PW30_2019_GranCanaria/Projects/NeuroNetworkSegmentationofNeck/README.md
  43. +2 −2 PW30_2019_GranCanaria/Projects/OHIF.AI/README.md
  44. +1 −1 PW30_2019_GranCanaria/Projects/PointSetRegistration/README.md
  45. +1 −1 PW30_2019_GranCanaria/Projects/TrainingPrograms/README.md
  46. +2 −2 PW30_2019_GranCanaria/Projects/UpperAirwayAirflowSimulation/README.md
  47. +6 −6 PW30_2019_GranCanaria/Projects/Useof3DSlicerinTrainig/README.md
  48. +2 −2 PW30_2019_GranCanaria/Projects/ohif_dcm4chee_kubernetes/README.md
  49. +1 −1 PW30_2019_GranCanaria/Projects/ohif_web_components/README.md
  50. +1 −1 PW31_2019_Boston/Projects/ClubfootCasts/README.md
  51. +1 −1 PW31_2019_Boston/Projects/Connect_SPINE_and_XNAT/README.md
  52. +2 −2 PW31_2019_Boston/Projects/NeuroSegmentation/README.md
  53. +1 −1 PW31_2019_Boston/Projects/OHIFPluginArchitecture/README.md
  54. +2 −2 PW31_2019_Boston/Projects/PythonPackages/README.md
  55. +1 −1 PW31_2019_Boston/Projects/ROS-MED/README.md
  56. +1 −1 PW31_2019_Boston/Projects/SlicerIMSTK/README.md
  57. +1 −1 PW31_2019_Boston/Projects/VolumeRenderingImprovements/README.md
  58. +1 −1 PW31_2019_Boston/README.md
  59. +1 −1 PW32_2019_London_Canada/README.md
@@ -6,7 +6,7 @@ Back to [Breakout Sessions List](../README.md#BreakoutSessions)
* AR/VR use cases
* VR demo stations
* Slicer VR interactions
* SlicerOpenVR sofware design discussion
* SlicerOpenVR software design discussion

## Presenters
- Sam Jang (Medical Augmented Intelligence): Clinical use cases
@@ -39,7 +39,7 @@ The FDA provides Guidance documents online. These are guidelines to help you pre

### Documentation Requirements

A quality system needs to be in place that includes a software standard operating procedure document. It will detail your software release procedures that will have to be followed to release a vesion for the application.
A quality system needs to be in place that includes a software standard operating procedure document. It will detail your software release procedures that will have to be followed to release a version for the application.

In the Software section of the 510(k) application you have to provide:
* Risk Analysis
@@ -50,7 +50,7 @@ In the Software section of the 510(k) application you have to provide:

| Name | Description | How Used in the Software |
| ------- | ----------- | ------------------------ |
| 3D Slicer 4.6.2 | url, description from web page | ... uses the visualization and data struture of 3D Slicer. ... uses the following modules from 3D Slicer: DICOM database, 2D and 3D image data structures, loading, and visualization modules( list). It also contains the VTK, ITK and Slicer Execution Model libraries that are used. |
| 3D Slicer 4.6.2 | url, description from web page | ... uses the visualization and data structure of 3D Slicer. ... uses the following modules from 3D Slicer: DICOM database, 2D and 3D image data structures, loading, and visualization modules( list). It also contains the VTK, ITK and Slicer Execution Model libraries that are used. |
| CTK # | |
| Qt # | |
| etc | |
@@ -67,7 +67,7 @@ These are notes from PW#27 Preparation Hangouts held weekly on Tuesdays at 10am
* Registration page to be done
* Andras agreed to provide segmentation help
* Tina wants to organize it
* Participants will be asked to provide sharable test data and to post results / tutorial on discourse site
* Participants will be asked to provide shareable test data and to post results / tutorial on discourse site

## Hangout #1: November 7th, 2017

@@ -13,7 +13,7 @@ The goal is to make available in Slicer this and other similar tools based on De

## Objective

1. Integrate a Lung Segmentation algorith based on Deep Learning in the Chest Imaging Platform.
1. Integrate a Lung Segmentation algorithm based on Deep Learning in the Chest Imaging Platform.
1. Make available these and other similar tools in Slicer

## Approach and Plan
@@ -13,7 +13,7 @@ Back to [Projects List](../../README.md#ProjectsList)
# Project Description
The Video streaming saving module Sequence. [Video Streaming in Sequence](https://drive.google.com/open?id=1gCdVS6aRlg__4KuaoDLK4HqSbAFGoZ4d)

A 5 minutes HD video without compression will be around 10GB. VP9, H264, HEVC codecs are availabe for video compression and video streaming.
A 5 minutes HD video without compression will be around 10GB. VP9, H264, HEVC codecs are available for video compression and video streaming.

## Objective

@@ -42,7 +42,7 @@ We are using Partner's image database for a corpus of imaging data (liver diseas
## Progress

1. We had a first team meeting to bring together computer scientists and clinicians.
1. Dr. Wall reviewed her progress in selecting a small set of optimal diseased and control patients. This process has been challenging beccause many people with liver disease have had surgery or tumor ablation that changes the liver morphology. It is also not possible to select only patients on 3T scanner before BWH began using EPIC (2015).
1. Dr. Wall reviewed her progress in selecting a small set of optimal diseased and control patients. This process has been challenging because many people with liver disease have had surgery or tumor ablation that changes the liver morphology. It is also not possible to select only patients on 3T scanner before BWH began using EPIC (2015).
1. Alireza Ziaei, Raul San Jose, and Randy Gollub are assisting with RPDR querying and image retrieval.
1. Jennifer worked on CITI training for IRB clearance to access the data. And talked with experts using PyRadiomics on MRI Data and their approaches on evaluating features (Michael Schwier and Joost van Griethuysen).

@@ -34,7 +34,7 @@ Provide an easy path for distributing extensions that use CUDA.
1. https://www.slicer.org/wiki/Documentation/Nightly/Developers/Tutorials/DashboardSetup
1. Do I need a separate extension server?
1. How do CUDA extensions get displayed in extension manager?
1. Options for distrubution
1. Options for distribution
1. Option A - End user must install CUDA SDK of same version
1. Option B - Extension must statically link CUDA libraries
1. Option C - Extension bundles shared libraries
@@ -21,7 +21,7 @@ Release a whole brain fiber clustering atlas for consistent white matter parcell

## Progress and Next Steps

- Done: a pre-release verion on GitHub (not public yet)
- Done: a pre-release version on GitHub (not public yet)

<!--Describe progress and next steps in a few bullet points as you are making progress.-->

@@ -19,7 +19,7 @@ My expertise is in Girder and scalable cloud based processing. I will give a ~15

### General thoughts

Commercial cloud services are good for experimentation without long term committment, and are useful when you need to have dynamic and elastic scaling. The providers are constantly rolling out new services, and there is a large amount of expertise encoded into these services (e.g. compare the cost of using AWS Elastic Load Balancer versus the time to gain the expertise of knowing how to run a load balancer), but the accounting model may have a mismatch with grant funded research (e.g. it may be easier to pay for an hour of someone's time to build a service versus paying for an hour of a cloud based service, even though the cloud based service is much cheaper in this comparison).
Commercial cloud services are good for experimentation without long term commitment, and are useful when you need to have dynamic and elastic scaling. The providers are constantly rolling out new services, and there is a large amount of expertise encoded into these services (e.g. compare the cost of using AWS Elastic Load Balancer versus the time to gain the expertise of knowing how to run a load balancer), but the accounting model may have a mismatch with grant funded research (e.g. it may be easier to pay for an hour of someone's time to build a service versus paying for an hour of a cloud based service, even though the cloud based service is much cheaper in this comparison).

To realize the full power of the cloud, a different mindset is in order compared to purchased hardware and software. Think about using extremely powerful and expensive cloud resources for a very short period of time, or using many more resources in the short term than you would otherwise.

@@ -70,7 +70,7 @@ Kitware has had good luck with
* [Terraform](https://www.packer.io/intro/index.html) - creates infrastructure, can target AWS, GCP, OpenStack
* [Ansible](https://www.ansible.com/) - configures and provisions software, as long as you have SSH and root access
* [Packer](https://www.packer.io/intro/index.html) - package provisioned compute resources into VMs, Vagrant files, Docker images, AMIs
* [Docker](https://www.docker.com/) - package executables and dependecies into a self-contained and portable container
* [Docker](https://www.docker.com/) - package executables and dependencies into a self-contained and portable container

We use these technologies for our project deployments, and have built reusable tooling on top of them for Girder and Resonant tools.

@@ -11,7 +11,7 @@ Back to [Projects List](../../FIXME.md#ProjectsList)

# Project Description

This project is a research collaboration between the public research institute IACTEC and the University of Las Palmas de Gran Canarias(ULPGC) in order to use InfraRed (IR) sensors and advanced image processing technics in 3D Slicer for medical diagnosis, mainly for foot ulcers detection in diabetic patients. Different infrared cameras shall be connected to 3D Slicer using the PLUS toolkit and a new medical thermal infrared extension shall be developed.
This project is a research collaboration between the public research institute IACTEC and the University of Las Palmas de Gran Canarias(ULPGC) in order to use InfraRed (IR) sensors and advanced image processing techniques in 3D Slicer for medical diagnosis, mainly for foot ulcers detection in diabetic patients. Different infrared cameras shall be connected to 3D Slicer using the PLUS toolkit and a new medical thermal infrared extension shall be developed.

## Objective

@@ -23,7 +23,7 @@ This project is a research collaboration between the public research institute I
## Approach and Plan

1. Create a new Slicer module for processing thermal infrared images.
2. Review segmentation, registration and other image processing technics for foot ulcer detection with infrared images.
2. Review segmentation, registration and other image processing techniques for foot ulcer detection with infrared images.
3. Testing.
4. Assessment of live video streaming using ffmpeg.

@@ -38,7 +38,7 @@ making progress.-->
3. The next objetives are :
- To finish the images registration.
- To integrate new infrared cameras, like Thermal Expert Q1 camera.
- To add some diferent segmentation methods in order to perform a comparisson.
- To add some different segmentation methods in order to perform a comparison.

# Illustrations

@@ -36,7 +36,7 @@ Back to [Projects List](../../README.md#ProjectsList)

## Progress and Next Steps

1. Identified main components in processing pipline to be seperated and made open for extension.
1. Identified main components in processing pipline to be separated and made open for extension.
1. Discussed refactoring options/software architecture.
1. Identified errors in current computations.
1. Refactored main component.
@@ -10,7 +10,7 @@ Back to [Projects List](../../README.md#ProjectsList)

# Project Description
Creating a program to generate 4D MRI sequences applying the retrospectiv stacking method on 2D MR slices.
The available data is comprised of an time resolved alternating sequence of navigator and data slices and a pure sequence of time resolved navigator slices. All navigator slices being aquired at the exact same location and the data slices "scanning" the complete liver in a cyclic manner. To generate a 4D MR sequence from that the program has to collect all data frames that were aquired during the same breating phase, i.e. not at the same time but at different times during the same breathing phase. To find these, the navigator slices are utilized. Finding correspondances between the navigator slices of the pure navigator sequence and the once of the alternating sequence means to find similar or same breathing phases. Thus we find all corresponding data slices giving the 3D liver at the specific breathing phase using the correspondance of its encompassing navigator slices.
The available data is comprised of an time resolved alternating sequence of navigator and data slices and a pure sequence of time resolved navigator slices. All navigator slices being acquired at the exact same location and the data slices "scanning" the complete liver in a cyclic manner. To generate a 4D MR sequence from that the program has to collect all data frames that were acquired during the same breating phase, i.e. not at the same time but at different times during the same breathing phase. To find these, the navigator slices are utilized. Finding correspondences between the navigator slices of the pure navigator sequence and the once of the alternating sequence means to find similar or same breathing phases. Thus we find all corresponding data slices giving the 3D liver at the specific breathing phase using the correspondence of its encompassing navigator slices.

## Objective

@@ -26,16 +26,16 @@ The available data is comprised of an time resolved alternating sequence of navi
<!--Describe progress and next steps in a few bullet points as you are making progress.-->
- got insight in available and relevant DICOM tags (big thanks to Joost for the DICOM Explorer)
- hit a roadblock when data appeared to be faulty
- wrote a python script sorting the data by aquisition time, turns out data is faulty after all (thanks to Joost again)
- wrote a python script sorting the data by acquisition time, turns out data is faulty after all (thanks to Joost again)

**next steps**
- figure out how to tell the MR scanner to aquire slices in the right order
- figure out how to tell the MR scanner to acquire slices in the right order

# Illustrations

<!--Add pictures and links to videos that demonstrate what has been accomplished.-->

![Data aquisition](dataAquisition.PNG)
![Data acquisition](dataAquisition.PNG)
![Data sorting](dataSorting.PNG)
![Data fault](TimeSorted.PNG)

@@ -19,7 +19,7 @@ Back to [Projects List](../../README.md#ProjectsList)

## Approach and Plan

1. Brainstorming the usefull utilities for mpMRI reading of prostate
1. Brainstorming the useful utilities for mpMRI reading of prostate
1. Exploring dcmjs library


@@ -35,7 +35,7 @@ to clinical scanners.
- Follow excellent QIICR tutorial instructions
- DCMQI conversion to DICOM segmentation object failed during the first attempt. Consulted Andrey.
- One of slices from the Phillips small animal scanner was identified with inconsistent header contents compared to other slices.
- Change made to DCMQI to accomodate this dataset.
- Change made to DCMQI to accommodate this dataset.
- Reprocessed successfully and measured DICOM segmentation object using Quantitative Reporting module
- Build and trained a CNN using Keras. Consulted with Alireza about how to connect with DeepInfer.
- Planning to complete DeepInfer integration of our new model over the coming weeks.
@@ -21,7 +21,7 @@ Back to [Projects List](../../README.md#ProjectsList)

## Approach and Plan

1. Review/discuss approches from current literature
1. Review/discuss approaches from current literature
1. Investigate the results on the Prostate MRI test-retest data
1. Draft a paper on pyradiomics repeatability evaluated on the Prostate MRI test-retest data

@@ -25,7 +25,7 @@ The main purpose of this project is to create a module that integrates an alread
## Progress and Next Steps
1. Added a model node to show a sphere within the image on slicer
2. Added a cross hair fiducial to collect the image coordinate from the center of the straw
3. Created a loable extention to connect the python module to the C++ code
3. Created a loable extension to connect the python module to the C++ code
4. Built slicer on my computer
5. Built openCV 3.3
6. Made the view Red view only
@@ -53,7 +53,7 @@ SlicerSALT will be used to:
- Shape Regression Extension:
- Fixing of some bugs on the shape regression computation
- Adding of some tests
- Test of the slicer extension package on Windows and Mac (Issue on Linux) -> Almost ready to be intergrated in SlicerSALT
- Test of the slicer extension package on Windows and Mac (Issue on Linux) -> Almost ready to be integrated in SlicerSALT
- Estimation of shape correspondence for population of objects with complex topology:
- Comparison of the three methods already existing
- Abandon of the ThinShellDemon method due to the generated results not enough accurate
@@ -36,7 +36,7 @@
* Findability from google search
* Find the best tutorial for the topic of interest (jump to the content)
* Stackoverflow-like model
* Is there a way to encourage people end-users to contribute to the documentaion
* Is there a way to encourage people end-users to contribute to the documentation
* Use case libraries
* Registration Use Case Library on wiki
* Segmentation Recipe github page
@@ -4,7 +4,7 @@ Back to [Projects List](../../README.md#ProjectsList)

## Key Investigators

- [Davide Punzo](https://punzo.github.io/) (Kapteyn Astronomical Institue, Netherlands)
- [Davide Punzo](https://punzo.github.io/) (Kapteyn Astronomical Institute, Netherlands)
- [Andras Lasso](http://perk.cs.queensu.ca/users/lasso) (Queen's University, Canada)
- [Steve Pieper](https://lmi.med.harvard.edu/people/steve-pieper) (Isomics Inc., USA)
- [Jean-Christophe Fillion-Robin](https://www.kitware.com/jean-christophe-fillion-robin/) (Kitware Inc., USA)
@@ -36,7 +36,7 @@ The 3D view controller widget should have GUI for synchronizing the following pr

let's leave as it is

* shall we add GUI for recent volume rendering varibales moved from the MRMLVolumeRendering to the MRMLView node (Csaba mod to volume rendering)? Probably adding also this will be confusing (i.e. duplication of GUI and sync with volume rendering GUI).
* shall we add GUI for recent volume rendering variables moved from the MRMLVolumeRendering to the MRMLView node (Csaba mod to volume rendering)? Probably adding also this will be confusing (i.e. duplication of GUI and sync with volume rendering GUI).

not necessary!

@@ -50,7 +50,7 @@ The 3D view controller widget should have GUI for synchronizing the following pr

* (b) different angle of view for second (third, etc.) linked camera? different camera motion, etc...

specilized interface in the cameras module. It will be designed and implemented later on
specialized interface in the cameras module. It will be designed and implemented later on

* (c) Display content as in the 2D view? shall add models too? segmentation maust always be global (for 2d/3d etc...)??

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