Quantitative Image Analysis Workshop 3D Slicer User Training NCI Advanced Biomedical Computing Center NCI Small Animal Imaging Program Dr. Yanling Liu Dr. Joseph Kalen David Mott Dr. Curtis Lisle Nimit Patel August 28, 2012

Training Agenda Access to Slicer: Slicer website and SAIP remote desktop Working the Slicer user interface: adjusting image views, window/level, image threshold, and UI layout Basic dataset input/output: How to load a volume for analysis, save out results, and resume session later. ROI: Definition and uses of Region of Interest selection in Slicer; volume cropping Segmentation: selecting and measuring volumes

3D Slicer Website 3D Slicer has been developed over years with contributions from over 20 universities and funding from multiple NIH research grants The website, hosts the program itself and many additional resources, datasets, and training materialshttp:// Slicer is available for Windows, Mac, and Linux workstations SAIP remote desktop Microsoft Remote Desktop connection with machine name saip- remote.ncifcrf.gov slicer.org slicer download page “Download” link “For Users” link offers additional tutorials

The Slicer User Interface By default, Slicer offers slice views and a 3D perspective view of loaded datasets An operation panel on the top left lets users select from different Slicer modules, each offering sets of related operations volume display segmentation Region Of Interest

Slicer User Interface Module controls are placed along the top menu load/save data select the module to use module history menu module back & forward buttons show volume info select user interface layout add fiducial points or measurements

Dataset Input / Output Add Data - The simplest way to view a new volume, image, or other data with Slicer Even if the dataset is stored in multiple files, select the first slice only and click Open the file loading dialog:

Exercise #1 Start up Slicer Explore the selection of different modules Change the layout back and forth between a single axial slice (red, green, or yellow) and back to the standard layout Open the sample dataset so it shows in the axial windows

Displaying a Volume step #1: Select from presets to quickly adjust the image contrast If step #1 does not provide a good contrast, the Window / Level can be adjusted for fine control, but this control can be hard to use Easier way: Click & hold left mouse button, drag mouse cursor in slice view to change window/level

Working with Slice Views A little pop-down menu controls the behavior of each slice view Background (standard) volume being observed by slice viewer Should controls apply to ALL slice views (linked mode) Display slice in 3D view Move the slice back & forth within the volume labelmap to display as overlay

A Lightbox view of a Volume To view a volume using a lightbox display (display all slices), select a slice view (e.g. red, green, or yellow), then expand the drop-down control panel The push pin icon fastens the panel open when it is selected Select the desired lightbox mode (e.g. 3x3). Moving the mouse up & down in the window scrolls through the dataset this expands the control panel lightbox icon

Saving a Slicer Session Slicer saves progress of analysis sessions in a supplementary file with the “.mrml” extension. We recommend creating a new directory on the workstation, then saving the data and.mrml files all within this directory The result is the data and scene stored together in a single directory step #1 - select the Save Data icon step #2 - in the save window, select the “force all data to be saved” checkbox step #3 - select the new output directory and click the Save button (shown on next slide)

File Save Dialog Save dialog generally saves only files that have been modified since last save; files to be saved are marked with a check mark Forcing a save into a new directory assures all components of a scene are stored together. Click here to open dialog and select or create directory to save to

Exercise #2 Practice adjusting the contrast and window/level view of the current volume select different slice views and enable/disable the lightbox view Save the current session Create a new output directory Save the session into the new directory by forcing save on all items and clicking the Save button Restart Slicer and load the saved session

Segmenting Image Features Slicer has several ways to select features in an image (for example, to mark a tumor and calculate its size) We want to label all pixels occupied by each feature with a unique number (for example: assign tumor pixels label of 1) Labels are stored as labelmaps (an image where each spot in the image is the label number feature to segment labelmap label shown in context + label value = 0 outside label value = 1 inside tumor

First Crop the Volume Automatic segmentation algorithms can become confused easily between a tumor and healthy tissue Therefore, we will create a small volume containing only the tumor and as little else as possible Select the Crop Volume module a small volume containing only the tumor

Crop Volume Module This module creates a new, smaller dataset containing only what is inside an adjustable bounding box select Input, default is current volume Select Create new ROI enable ROI (region of interest) visibility Adjust ROI Isotropic gives best results Select Crop button to create a new volume

Adjusting the ROI Observe the bounding box in the 2D slice views Click and drag the outlines on any slice (or the perspective display) until the ROI contains only the feature of interest Zooming and moving around in views will help click & drag any dot Right mouse button and drag down zooms in Shift + right button drag moves the views

Exercise #3 Practice zooming and moving around in the 2D views Select the Crop Volume module Position the ROI until it as close around a feature as possible Crop and notice how the viewers change to focus on the newly created volume Practice switching between volumes being observed

Segmentation Options Segmentation is complicated Expect it to take awhile to master Multiple choices in Slicer: Grow/Cut operation in editor (works well but takes too long to explain today) OTSU Segmentation Simple Region Growing OTSU segmentation example in Slicer

OTSU Segmentation An automatic algorithm to separate a volume into two classes: (1) feature and (2) everything else Verify the input volume and that the correct ROI node is being observed The input volume should be the output of the Crop volume operation Create a new output volume for the labelmap

Determining Feature Volume The Quantification / Label Statistics module will calculate the volume of the labels generated by OTSU segmentation Select the input volume as the grayscale volume and select the labelmap output from the segmentation. Hit Apply to generate the chart of volumes for each label

Exercise #4 Run the OTSU Segmentation module on the dataset Paint labels as necessary in the Editor module Calculate the volume of the segmented feature

Training Session Summary Segmentation results are often not perfect In many cases, algorithms more sophisticated than OTSU are needed, or the segmentation will have to be hand edited later This tutorial was designed to introduce everyone to dataset management and basic segmentation The Small Animal Imaging Program and the ABCC Imaging and Visualization Group will gladly provide further assistance, as needed. Thanks for attending the training session!

Extra Slides

Grow/Cut Segmentation Very powerful segmentation algorithm in Slicer Requires use of the labelmap Editor module User “smudges” labelmap value 1 inside the region of interest and value=2 outside area Algorithm determines 3D boundary of area user-provided guidance algorithm result

Grow/Cut Process In Editor, create a new labelmap Select Paintbrush Icon with label=1; smudge a region inside the feature/organ Change label value to label=2; smudge a region outside the feature Click Grow/Cut icon Select Apply button