[ITK] CT liver segmentation

[Dženan Zukić]

Hi Jan,

the code was fine, you missed to match the seed position to the value
coming out of the watershed filter (imgWS) in that location. As you were
feeding the output of the watershed filter to the segmentation filter, you
needed to give the thresholds related to the values of that image and not
the original input image (imgSeries).

My modification is attached. ImageJ connection did not work out of the box
for me so I wrote intermediate images to disk in order to inspect them.

Regards,
Dženan

On Sun, Dec 27, 2015 at 4:49 PM, Jan Witowski <
jan.witowski at student.uj.edu.pl> wrote:

> Hello Dženan,
>
> Thanks for your assistance.
> Full code of my example is located here:
> https://gist.github.com/jwitos/9abe7124e28ce2e31b6e
> I’ve also uploaded the DICOMs I’ve been using (30~MB, 152 .dcms inside) if
> you want to test them out: http://cm-uj.pl/files/cleaned.zip
>
> Best,
> Jan
>
> On 27 grudnia 2015 at 17:35:24, Dženan Zukić (dzenanz at gmail.com) wrote:
>
> Hi Jan,
>
> that sounds like some silly simple mistake. If you provided a short
> compilable example with the accompanying input, somebody will probably be
> able to point out the problem.
>
> Regards,
> Dženan
>
> On Sun, Dec 27, 2015 at 10:20 AM, Jan Witowski <
> jan.witowski at student.uj.edu.pl> wrote:
>
>> Hello,
>>
>> I am working with CT abdomen images and trying to segmentate the liver
>> (liver itself, vessels and the HCC tumor). I managed to do it using OsiriX
>> which was rather manual segmentation, and now I am working to do the same
>> using more accurate and semi-automatic algorithms with usage of SimpleITK.
>> I don’t really have much experience with ITK and image manipulation which
>> is why I ask you guys for some help.
>>
>> Currently, I want to try the watershed method. I loaded my DICOM series,
>> ran the GradientMagnitude filter and proceeded with MorphologicalWatershed
>> (using arg Level=17, value chosen manually). I got pretty nice output
>> image, I guess:
>> http://jwitowski.com/content/images/2015/12/WSsegmentation-4469-0.jpg
>>
>> But now, the question is - how do I take that liver out of the image
>> above? My best guess is, I should run a region growing segmentation.
>> I tried to do that, but - no matter what parameters I choose - I always
>> get the all-black image as an output. The whole image is black. I tried
>> different thresholds and other starting seeds, but… no result.
>> Here is a gist of my segmentation part:
>> https://gist.github.com/jwitos/4b953b3ef873fee3965c
>>
>> I would really appreciate any help or tip
>>
>>
>> Best,
>> Jan Witowski
>> Collegium Medicum Jagiellonian University in Krakow
>>
>> _______________________________________________
>> Community mailing list
>> Community at itk.org
>> http://public.kitware.com/mailman/listinfo/community
>>
>>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://public.kitware.com/pipermail/community/attachments/20151228/a0093ec4/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: watershed.py
Type: application/octet-stream
Size: 2199 bytes
Desc: not available
URL: <http://public.kitware.com/pipermail/community/attachments/20151228/a0093ec4/attachment-0001.obj>