Assignment 4: Segmentation and Preprocessing
Methods In Medical Image Analysis (BioE 2630 : 18-791 : 42-735) - Spring 2017

Creative Commons License ITK Segmentation and Preprocessing Assignment by Vikas Revanna Shivaprabhu and John Galeotti, © 2012-2017 Carnegie Mellon University, is licensed under a Creative Commons Attribution 3.0 Unported License. Permissions beyond the scope of this license may be available by sending email to itk ATgaleotti.net. This assignment was made possible in part by NIH NLM contract# HHSN276201000580P.

55 points total

Due Date: Your final submission must be committed to svn by 5 pm EST on Friday April 7. 10 PM EST on Wednesday, March 29. Big problems may not show up until the end, so finish early!

E-mail your TA or instructor with questions or problems.

This is your first real programming assignment, and your first use of SVN. Upon completing this assignment, you should feel comfortable not only using ITK, but also programming for arbitrary pixel types and dimensionality. This assignment requires that you have ITK or SimpleITK installed and working. This assignment includes:

  1. Using SVN
  2. Region-Growing Threshold Segmentation
  3. Global Threshold Segmentation
  4. Fast Marching
  5. Creative Experimentation

For further guidance you should consult the ITK software guide and the lecture slides, especially those from the background lectures on programming and ITK basic usage, and the segmentation lectures on Overview and Thresholding, Active Contours, and Level Sets.

0. Using SVN (3 points, apx. 0.5-2 hours)

Usage of SVN was discussed in ITK Background Lecture, starting on slide 13. Note, if installing a command-line svn program such as TortiseSVN, then be sure to have it install the command-line svn tools as well. You will be required to submit your code for all remaining assignments using SVN. You should have received your SVN username and password either in class or via email. All of the instructions given here will be for the command line version of SVN. You may want to use a GUI instead (see the above-mentioned lecture slides), but it is up to you to learn the interface of the GUI of your choice.

Begin by confirming access to your SVN module, by listing the current contents of your module (the following {} denote something for you to replace, and the {} characters should not be typed):

svn list -R --verbose --username {Your_SVN_User_Name} https://biglab.ri.cmu.edu/svn/mimia17/{Your_SVN_User_Name}
{Provide your SVN password when prompted.}

Your password should be accepted, and you should not have any connection errors.

(If you are prompted about the server certificate beloning to vialab.sp.ri.cmu.edu insterad of biglab.ri.cmu.edu, then please just permanantly accept the certificate. Alternately, you could just always use the svn server's primary name of vialab.sp.ri.cmu.edu instead of the biglab.ri.cmu.edu shortcut, as follows https://vialab.sp.ri.cmu.edu/svn/mimia17/{Your_SVN_User_Name}.)

Change to the directory you previously created (in HW2) to contain ITK, VTK, etc. (e.g. cd c:\MIIA), and then checkout your module:

svn co --username {Your_SVN_User_Name} https://biglab.ri.cmu.edu/svn/mimia17/{Your_SVN_User_Name}
{SVN will probably remember your password for you, but if not then provide it again when requested.}

You should now have a new directory named C:\MIIA\{Your_SVN_User_Name}. This directory should contain all of your code for the remainder of this class (but none of your binaries unless specifically instructed otherwise).

Now add a directory to your SVN module to contain this assignment. You must first create the directory locally, then tell SVN to add it to your module, and then commit your changes to the SVN server:

cd c:\MIIA\{Your_SVN_User_Name}
mkdir hw3
svn add hw3
svn ci hw3 -m "Setting up module for hw3"

This is the general template you must follow to add any new file or directory to your SVN repository. When you change a file that already exists, you only need to run the last line above (the commit line) to update the server's copy of your file. Notice the -m option, followed by a quoted string. SVN requires that every committed change be given a brief description, and this is how to do it. (Warning: If you get strange errors when trying to commit, then make sure you are using the double-quote characters around your commit description.)

Now, to further test and exemplify things, you are going to create a directory for this part of the assignment, add a file containing the one word "Hello", commit the directory + file, change the file by adding the word "World" to the end, and then commit the change:

cd c:\MIIA\{Your_SVN_User_Name}\hw3
mkdir Part0
echo "Hello"> Part0\file.txt
svn add Part0
svn commit Part0 -m "Initializing Part 0"
echo "World">> Part0\file.txt
svn commit Part0 -m "Fixed file.txt"

In the above, if you prefer you could also use a text editor, such as Windows Notepad, instead of the echo commands. Also, as you may have noticed, running an svn command on a directory runs the command on all of the files and directories it contains. This can be nice...or not. Use with caution.

In the future, the source code for each part of each assignment should be in an appropriately numbered hw#\Part# directory structure, but...

NEVER build C++ source code inside your svn module. For C++ always do an out-of-source build!

0.1 SVN Setup for this segmentation assignment & Dataset Acquisition (2 points, apx. 5-30 minutes)

Create a new svn directory structure for this assignment, following the pattern below exactly. Assuming you are currently in the directory c:\MIIA\{Your_SVN_User_Name}\hw3, this would look like:

mkdir Part1
mkdir Part2
mkdir Part3
svn add Part1 Part2 Part3
svn ci Part1 Part2 Part3 -m "Setting up module for segmentation hw"

Now, download the image data set you will be using for this assignment, and unzip the archive somewhere convenient, but NOT in your SVN directory. (Please do NOT upload your copy of these downloaded images to SVN.)

Inside the resulting SegmentationAssignment_Images directory, you will find IM.mha, which is a small (64x64x64) piece of a much larger volumetric CT data. IM.mha has been cropped to include the ascending aorta, which will be your primary segmentation task for this assignment. In the same directory you will also find SegIM.mha, which is a coresponding expert segmentation for the ascending aorta, as visible in IM.mha. You will also find IM_2D.png and its corresponding expert segmentation SegIM_2D.png, which is a 2D image slice extracted from the 3D image IM.mha. As noted in the included seed_data.txt, your automatic segmentations of this piece of the aorta should be initialized using the seed point (x=35,y=34) for 2D and (x=35,y=17,z=28) for 3D.

Run the ITK-SNAP program (which you installed in HW2, Part 1). From the initial dialog, choose "Open Image ..." and then choose "Browse...". Browse to the SegmentationAssignment_Images directory, and choose the file IM.mha. Choose "Next >" and then choose "Finish" (don't worry about the "Loss of Precision" warning). Nowon the top menu bar go to "Segmentation" -> "Open Segmentation ...". As you just did, browse to the SegmentationAssignment_Images directory again, but this time choose the file SegIM.mha. Once the segmentation is loaded, click the "update" button at the bottom of the main window. A screen-shot of what this should look like after you update SNAP's mesh is included in the file SegHW-SNAP.png. Spend a little time using SNAP to look through the volumetric data, to get a feel for it.

1. Region-Growing Threshold Segmentation (10 points)

Begin by copying c:\miia\SimpleITK\Examples\Python\ConnectedThresholdImageFilter.py from your SimpleITK source directory to Part1\threshold.py. Note that this program is made to be directly executable from the normal command line, by entering something like ipython Part1\threshold.py. When you run the program without any command line arguments, it will tell you what arguments you should use.

SimpleITK Python vs. SimpleITK C++ vs. "Full" ITK C++
From this point on, we will assume that you want to use SimpleITK in Python. However, if you prefer to use SimpleITK in C++ instead, then rather than grabbing the .py example files, look in the same directory for the corresponding .cxx example files and use those instead. If you use C++, you will of course also need to submit an appropriate CMakeLists.txt file as well. Finally, if you're really brave, then feel free to try this in "full" ITK instead of SimpleITK, but keep in mind that you may need content from future lectures (try looking at last year's course website). You should be able to find equivalent "full ITK" example files in ITK's Examples directory.

Reminder: Remember that matched indentation is very important in Python, and you should always uses spaces (not tabs, since they are treated differently).

Test the code using 2-dimensional image IM_2D.png with the provided seed point (from seed_data.txt) and compare your result with SegIM_2D.png.

Make the following changes to threshold.py, being sure to commit your changes to svn after you complete each step:

Big Hint: Be aware that 3D CT images have 12-bit data stored in 16-bit pixels, and so their intensities can be negative or exceed 1000 (unlike standard jpegs or pngs that have a maximum intensity of 255).

Reminder: When working on any part of any assignment for this class, be sure to add any new source files to svn as soon as you create them, and be sure to frequently commit you code as you make changes to it. Above all, don't forget to committ your final code to svn!

2. Global Threshold Segmentation (10 points)

Begin by copying code from part 1 of the assignment Part1\threshold.py to Part2\globalThreshold.py.
Make the following changes to globalThreshold.py, being sure to commit your changes to svn after you complete each step:

Final reminder: Don't forget about adding and committing to svn! You can verify what is actually on the server either with the "svn list" command (mentioned at the beginning of this asignment), or by pointing your web browser directly to your svn URL.

3. Fast Marching (15 points)

Implement a fast marching solution to a level set evolution problem by building the following sequence of filters:
reader -> smoothing -> gradientMagnitude -> sigmoid ->  fastMarching -> thresholder -> writer

4. Creative Experimentation (18 points)

You have previously tried some of the SimpleITK iPython tutorial notebooks, and now you should read through (and I suggest experiment a little with) the Level Set tutorial notebooks.

Now, find your best combination of preprocessing and segmentation algorithms for the dataset.

Since this part is a competition to see who can get the best segmentation, you are on your own. Neither the TA nor myself will help you choose or implement your segmentation algorithm (but feel free to ask for help with compiler problems, etc.). Feel free to use all other sources of help at your disposal, including internet mailing lists (but please be polite and only ask specific questions on the itk mailing list). Place your well-commented code in Part4\best_segmentation.py (or for C++ use an .cxx extension and include an appropriate CMakeLists.txt file).

*** Also, be sure to include a comment at the top of best_segmentation.cxx indicating how long it takes your code to run on your machine, so that the TA has some idea how long he should wait for your code to finish executing.

Final Check

Finally, be sure all of your files are committed correctly, either by pointing your web browser to your svn URL and logging in with your svn name and password, or else by using the command "svn list -R --verbose --username {Your_SVN_User_Name} https://biglab.ri.cmu.edu/svn/mimia17/{Your_SVN_User_Name}". If these methods do not show all your submitted files present and with appropriate timestamps, then you have not properly committed them. In this case, the first thing to check is to make sure you've added the files to svn before comitting. Otherwise, email your TA and instructor for help.