[ITK-users] CropImageFilter, multithreading

[Lowekamp, Bradley (NIH/NLM/LHC) [C]]

Hello Jonas,

So ITK ( and therefore SimpleITK ), by default multi-thread each filter by default. This enables efficient processing of larger images.

However, for your task you can run a large number of filters concurrently. Fortunately, SimpleITK does support concurrent execution of ITK filters with light weight python Threads! This is an uncommon feature for Python libraries and is a distinguishing feature of SimpleITK.

I have been planning on writing an example or notebook on this.  Here is an efficient and compact code to accomplish your task as I understand it:

import SimpleITK as sitk
from multiprocessing.pool import ThreadPool

p = ThreadPool()

# https://github.com/blowekamp/itkOBBLabelMap/tree/master/test/data
img = sitk.ReadImage(“~/Downloads/jelly_beans.png")
seg = sitk.ReadImage(“~/Downloads/jelly_beans_seg.png”)

shapeStats = sitk.LabelShapeStatisticsImageFilter()
shapeStats.Execute(seg)

def extract_bb(img, shape_stats_filter, label):
    [x,y,xsize,ysize]=shape_stats_filter.GetBoundingBox(label)
    return sitk.RegionOfInterest(img,size=[xsize,ysize],index=[x,y])

bbimg_list = p.map(lambda label: extract_bb(img, shapeStats, label), shapeStats.GetLabels())


This uses advance concepts of multi-threading, closures, mapping, and thread pools. I think it is the integration of SimpleITK any Python at its bests!

One tweak which could be made to this code is to create a RegionOfInterestImageFilter object, and explicitly set it’s number of threads to 1, so that it is not multi-threaded.

A related not is the recently Oriented Bounding Box computation has been added to ITK’s LabelShape objects and filters, this is starting to get propagated into SimpleITK now. This can be used for a similar purpose but with a resample image filter to change the orientation of the separate object.

Enjoy!
Brad

On May 22, 2017, at 5:59 AM, Jonas Teuwen <jonasteuwen at gmail.com<mailto:jonasteuwen at gmail.com>> wrote:

Dear all,

Currently I have SimpleITK code to extract patches from a 3D medical image to train a neural network with. I do this with CropImageFilter and check if they are on the edge or not, and pad if necessary.

Currently this is done offline, so speed is not really an issue, however, I would like to do this online now, so load the image and mask, and return the patches. If I want to extract many small patches, about 1000 out of a large image (~3000x3000x50 or so) is there any reason why I would not use OpenMP instead of ITK's possibilities? I do not have a good understanding of the multithreading capabilities yet, so any pointers would be great.

Best,
Jonas Teuwen

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