Implement `NrrdReader` for `nrrd` and `seg.nrrd` files

Is your feature request related to a problem? Please describe. For one project we are working with seg.nrrd files to create pixelwise segmentations. However, when trying to load these files with LoadImaged I get the following error:

TemplateTypeError: itk.Image is not wrapped for input type `itk.Vector[itk.SS,3], int`.

To limit the size of the package, only a limited number of
types are available in ITK Python. To print the supported
types, run the following command in your python environment:

    itk.Image.GetTypes()

Possible solutions:
* If you are an application user:
** Convert your input image into a supported format (see below).
** Contact developer to report the issue.
* If you are an application developer, force input images to be
loaded in a supported pixel type.

    e.g.: instance = itk.Image[itk.RGBPixel[itk.UC], int].New(my_input)

* (Advanced) If you are an application developer, build ITK Python yourself and
turned to `ON` the corresponding CMake option to wrap the pixel type or image
dimension you need. When configuring ITK with CMake, you can set
`ITK_WRAP_${type}` (replace ${type} with appropriate pixel type such as
`double`). If you need to support images with 4 or 5 dimensions, you can add
these dimensions to the list of dimensions in the CMake variable
`ITK_WRAP_IMAGE_DIMS`.

Describe the solution you’d like When reading seg.nrrd files with pynrrd the files are handled correctly. I would thus propose to implement a NrrdReader based on pynrrd. I have already written the NrrdReader for private use and would be happy to make a PR.

Describe alternatives you’ve considered I have iterated through all supported pixel types, but this either raises an error or leads to incorrect import of the images. For example, some images have format [4, H, W, 1]. When forcing pixel type to itk.Image[itk.RGBPixel[itk.UC], int], the image is read as [3, H, W, 1], thus omitting a layer. This is a problem, as the number of layers in the different seg.nrrd files varies.

[lassoan]

The main advantage of using standard coded entries for describing what is in a segment because then you don’t need to hardcode across your data set that label value 2 means liver and label value 3 means tumor. These label values can only be used in a single data set because these arbitrary label values are chosen differently in each project. In contrast, if you specify that there is a (SCT, 10200004, 'Liver') and a (SCT, 4147007, 'Mass') segment in the segmentation then this information is remains valid forever, in every context. This is the same way how DICOM specifies what is in a segmentation.

DICOM recommends using SNOMED Clinical Terms (and you are granted free license to use all the terms that are part of the DICOM standard). You can find a list of codes that the DCMQI project assembled here: https://github.com/QIICR/dcmqi/blob/ac7d0fea3a4458a2e14bae0bac886813051b6ad4/doc/segContexts/SegmentationCategoryTypeModifier-SlicerGeneralAnatomy.json#L903-L918

You can find more details in this paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5675033/

[wyli]

I see, I think an NrrdReader might be useful here, please send a pull request…

CC @lassoan @thewtex in case they have better ideas about pixel_type in itk.imread in this case.