Dear all
I have a problem to understand how to use Segmentations nrrd file with image files in Python.
My problem is:
I’m able to open a stack of images and its segmentations using pynrrd.
However I can’t understand how to align the segmentation with the stack of images.
Reading the header of the segmentation I can’t understand how to shift the segmentation to align with the original image.
The same operation when performed in 3DSlicer is straightforward, and the alignment perfect.
So, I suspect that there is something in the header of the file that I need to understand.
Moreover I’m sure that there is a scale factor between segmentation and images.
Instead, the segmentation is too big (shape (43, 27, 5)) respect to original image ((96, 96, 19)). You have to think that we are in a case of a prostate cancer.
It seems that the segmentation is about 2 times the right dimension.
Can you give some advices on how to solve this problem?
Please remember that I’m not an expert user.
Thank you very much for your help
Andrea
This is an example of the code used and the results obtained on real images:
# Code for images:
filename = ‘image.nrrd’
readdata, header = nrrd.read(filename)
print(readdata.shape)
print(header)
(96, 96, 19)
OrderedDict([(‘type’, ‘unsigned short’), (‘dimension’, 3), (‘space’, ‘left-posterior-superior’), (‘sizes’, array([96, 96, 19])), (‘space directions’, array([[ 2.08301068, 0.03477655, -0.01159939],
[-0.03635903, 2.044063 , -0.40095127],
[ 0.00810046, 0.69308656, 3.53264395]])), (‘kinds’, [‘domain’, ‘domain’, ‘domain’]), (‘endian’, ‘little’), (‘encoding’, ‘gzip’), (‘space origin’, array([-96.8901825 , -92.38396454, -23.10687065]))])
# Code for segmentation
filename_seg = ‘image.seg.nrrd’
readdata_seg, header_seg = nrrd.read(filename_seg)
print(readdata_seg.shape)
print(header_seg)
(43, 27, 5)
OrderedDict([(‘type’, ‘unsigned char’), (‘dimension’, 3), (‘space’, ‘right-anterior-superior’), (‘sizes’, array([43, 27, 5])), (‘space directions’, array([[-6.24903227e-01, -1.04329815e-02, -3.47982208e-03],
[ 1.09077264e-02, -6.13218923e-01, -1.20285387e-01],
[-8.10047081e-03, -6.93086206e-01, 3.53264208e+00]])), (‘kinds’, [‘domain’, ‘domain’, ‘domain’]), (‘encoding’, ‘gzip’), (‘space origin’, array([ 26.12298371, -18.2445476 , -4.61884032])), (‘measurement frame’, array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])), (‘Segment0_Color’, ‘0.501961 0.682353 0.501961’), (‘Segment0_ColorAutoGenerated’, ‘1’), (‘Segment0_Extent’, ‘0 42 0 26 0 4’), (‘Segment0_ID’, ‘Segment_1’), (‘Segment0_Name’, ‘Segment_T2_ADC II’), (‘Segment0_NameAutoGenerated’, ‘0’), (‘Segment0_Tags’, ‘TerminologyEntry:Segmentation category and type - 3D Slicer General Anatomy list~SRT^T-D0050^Tissue~SRT^T-D0050^Tissue~^^~Anatomic codes - DICOM master list~^^~^^|’), (‘Segmentation_ContainedRepresentationNames’, ‘Binary labelmap|’), (‘Segmentation_ConversionParameters’, ‘Compute surface normals|1|Compute surface normals. 1 (default) = surface normals are computed. 0 = surface normals are not computed (slightly faster but produces less smooth surface display).&Crop to reference image geometry|0|Crop the model to the extent of reference geometry. 0 (default) = created labelmap will contain the entire model. 1 = created labelmap extent will be within reference image extent.&Decimation factor|0.0|Desired reduction in the total number of polygons. Range: 0.0 (no decimation) to 1.0 (as much simplification as possible). Value of 0.8 typically reduces data set size by 80% without losing too much details.&Default slice thickness|0.0|Default thickness for contours if slice spacing cannot be calculated.&Fractional labelmap oversampling factor|1|Determines the oversampling of the reference image geometry. All segments are oversampled with the same value (value of 1 means no oversampling).&Oversampling factor|1|Determines the oversampling of the reference image geometry. If it’s a number, then all segments are oversampled with the same value (value of 1 means no oversampling). If it has the value “A”, then automatic oversampling is calculated.&Reference image geometry|-0.624903227496551;0.01090772636925;-0.00810047080951418;96.9144821166992;-0.0104329814853039;-0.613218922740892;-0.693086206443774;94.4632263183594;-0.00347982208181081;-0.1202853868845;3.53264207871727;-33.7047958374023;0;0;0;1;0;319;0;319;0;24;|Image geometry description string determining the geometry of the labelmap that is created in course of conversion. Can be copied from a volume, using the button.&Smoothing factor|0.5|Smoothing factor. Range: 0.0 (no smoothing) to 1.0 (strong smoothing).&Threshold fraction|0.5|Determines the threshold that the closed surface is created at as a fractional value between 0 and 1.&’), (‘Segmentation_MasterRepresentation’, ‘Binary labelmap’), (‘Segmentation_ReferenceImageExtentOffset’, ‘116 166 14’)])