ValueError: conflicting sizes for dimension 'C' after converting one channel into ome.tiff image

[DorianKauffmann]

Hello,
Thanks for your work on BatchConvert, which is very useful and practical!

For my work in France-BioImaging, I need to convert several multichannel images into single-channel ome.tiff images - for each channel of the original images.
So I created this python code using your documentation; to convert here only the first channel of an example image).

img_omx = AICSImage( input_path/image_name.dv)
print(img_omx.dims)

<Dimensions [T: 1, C: 3, Z: 21, Y: 512, X: 512]>

command = ['batchconvert',
            'ometiff', '-pf', 'conda', 
            '-chn', str(0),
            input_path,
            output_path,
            ]    
try:
    subprocess.run(command, check=True)
except Exception as e:
    print(f"Error converting {input_folder_path}: {e}") 

But after running this code and converting my 3-channel images I got a Value error about channel sizes (idem with some imread)

img = AICSImage(ometiff_converted_demo)
print(img.dims)

ValueError: conflicting sizes for dimension 'C': length 1 on the data but length 3 on coordinate 'C' 

The full error message below:

ValueError                                Traceback (most recent call last)
/home/mypath Cell 6 line 6
      <a href='vscode-notebook-cell://home/mypath#X15sZmlsZQ%3D%3D?line=3'>4</a> img = AICSImage(ometiff_converted_demo)
      <a href='vscode-notebook-cell://home/mypath#X15sZmlsZQ%3D%3D?line=4'>5</a> # print(img.metadata)
----> <a href='vscode-notebook-cell://home/mypath#X15sZmlsZQ%3D%3D?line=5'>6</a> print(img.dims)

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/aicsimageio/aics_image.py:574, in AICSImage.dims(self)
    566 \"\"\"
    567 Returns
    568 -------
    569 dims: dimensions.Dimensions
    570     Object with the paired dimension names and their sizes.
    571 \"\"\"
    572 if self._dims is None:
    573     self._dims = dimensions.Dimensions(
--> 574         dims=self.xarray_dask_data.dims, shape=self.shape
    575     )
    577 return self._dims

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/aicsimageio/aics_image.py:438, in AICSImage.xarray_dask_data(self)
    423 \"\"\"
    424 Returns
    425 -------
   (...)
    431 If the image contains mosaic tiles, data is returned already stitched together.
    432 \"\"\"
    433 if self._xarray_dask_data is None:
    434     if (
    435         # Does the user want to get stitched mosaic
    436         self._reconstruct_mosaic
    437         # Does the data have a tile dim
--> 438         and dimensions.DimensionNames.MosaicTile in self.reader.dims.order
    439     ):
    440         try:
    441             self._xarray_dask_data = (
    442                 self._transform_data_array_to_aics_image_standard(
    443                     self.reader.mosaic_xarray_dask_data
    444                 )
    445             )

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/aicsimageio/readers/reader.py:532, in Reader.dims(self)
    525 \"\"\"
    526 Returns
    527 -------
    528 dims: Dimensions
    529     Object with the paired dimension names and their sizes.
    530 \"\"\"
    531 if self._dims is None:
--> 532     self._dims = Dimensions(dims=self.xarray_dask_data.dims, shape=self.shape)
    534 return self._dims

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/aicsimageio/readers/reader.py:359, in Reader.xarray_dask_data(self)
    352 \"\"\"
    353 Returns
    354 -------
    355 xarray_dask_data: xr.DataArray
    356     The delayed image and metadata as an annotated data array.
    357 \"\"\"
    358 if self._xarray_dask_data is None:
--> 359     self._xarray_dask_data = self._read_delayed()
    361 return self._xarray_dask_data

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/aicsimageio/readers/ome_tiff_reader.py:334, in OmeTiffReader._read_delayed(self)
    331 # Create the delayed dask array
    332 image_data = self._create_dask_array(tiff, strictly_read_dims)
--> 334 return self._general_data_array_constructor(
    335     image_data,
    336     dims,
    337     coords,
    338     tiff_tags,
    339 )

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/aicsimageio/readers/ome_tiff_reader.py:286, in OmeTiffReader._general_data_array_constructor(self, image_data, dims, coords, tiff_tags)
    283 # Reset dims after transform
    284 dims = [d for d in out_order]
--> 286 return xr.DataArray(
    287     image_data,
    288     dims=dims,
    289     coords=coords,
    290     attrs={
    291         constants.METADATA_UNPROCESSED: tiff_tags,
    292         constants.METADATA_PROCESSED: self._ome,
    293     },
    294 )

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/xarray/core/dataarray.py:418, in DataArray.__init__(self, data, coords, dims, name, attrs, indexes, fastpath)
    416 data = _check_data_shape(data, coords, dims)
    417 data = as_compatible_data(data)
--> 418 coords, dims = _infer_coords_and_dims(data.shape, coords, dims)
    419 variable = Variable(dims, data, attrs, fastpath=True)
    420 indexes, coords = _create_indexes_from_coords(coords)

File ~/mambaforge/envs/environment/lib/python3.10/site-packages/xarray/core/dataarray.py:163, in _infer_coords_and_dims(shape, coords, dims)
    161 for d, s in zip(v.dims, v.shape):
    162     if s != sizes[d]:
--> 163         raise ValueError(
    164             f\"conflicting sizes for dimension {d!r}: \"
    165             f\"length {sizes[d]} on the data but length {s} on \"
    166             f\"coordinate {k!r}\"
    167         )
    169 if k in sizes and v.shape != (sizes[k],):
    170     raise ValueError(
    171         f\"coordinate {k!r} is a DataArray dimension, but \"
    172         f\"it has shape {v.shape!r} rather than expected shape {sizes[k]!r} \"
    173         \"matching the dimension size\"
    174     )

ValueError: conflicting sizes for dimension 'C': length 1 on the data but length 3 on coordinate 'C'"

Suprisingly, when using the BioformatReader, I can have access to the new monocanal image (but it takes much more time to open it).

img = AICSImage(ometiff_converted_demo, reader=BioformatsReader)
print(img.dims)

<Dimensions [T: 1, C: 1, Z: 21, Y: 512, X: 512]>

Then I checked the ome-type xml metadata file and saw that the sizeC is indeed 1 but, in Pixels, the information for the 3 channels remains instead of just the first.
It therefore seems that there was an error at this level during the conversion.

<?xml version="1.0" encoding="UTF-8"?> ...
<OME ...
	<Instrument ID="Instrument:0"> ...
	</Instrument>
	<Image 
		...
		<Pixels BigEndian="false" DimensionOrder="XYCZT" ID="Pixels:0" Interleaved="false" PhysicalSizeX="0.07999999821186066" PhysicalSizeXUnit="µm" PhysicalSizeY="0.07999999821186066" PhysicalSizeYUnit="µm" PhysicalSizeZ="1.0" PhysicalSizeZUnit="µm" SignificantBits="16" 
SizeC="1" SizeT="1" SizeX="512" SizeY="512" SizeZ="21"Type="uint16">
			<Channel EmissionWavelength="683.0" EmissionWavelengthUnit="nm" ExcitationWavelength="642.0" ExcitationWavelengthUnit="nm" ID="Channel:0:0" NDFilter="0.10000000149011612" SamplesPerPixel="1">
				<LightPath/>
			</Channel>
			<Channel EmissionWavelength="528.0" EmissionWavelengthUnit="nm" ID="Channel:0:1" NDFilter="0.25" SamplesPerPixel="1">
				<LightPath/>
			</Channel>
			<Channel EmissionWavelength="435.0" EmissionWavelengthUnit="nm" ExcitationWavelength="405.0" ExcitationWavelengthUnit="nm" ID="Channel:0:2" NDFilter="0.10000000149011612" SamplesPerPixel="1">
				<LightPath/>
			</Channel> 
			...
		</Pixels>
	</Image>
...

It therefore seems that there was an error at this level during the conversion.

The full xml file (as .txt) here : Info_HeLa_Mitotrack-647_BrField_Hoechst_sample-1_001_visit_1.txt

As I can't upload .dv and ome.tiff images on GIthub, don't hesitate to contact me if you want the input and output image.

Thank you for all,
Dorian

[bugraoezdemir]

Hi @DorianKauffmann ,

Thank you for your positive feedback on BatchConvert and the detailed description of the issue. I can replicate the behaviour using several example images in different formats. The channel information of the original dataset is retained in the metadata of the extracted series.

It appears that the root cause lies within the Bio-Formats tools that are wrapped by BatchConvert for conversion.
I will investigate further to clarify if this behaviour of Bio-Formats is intentional or a potential bug. In the meanwhile I will also try to figure out a temporary fix in BatchConvert.

Cheers,
Bugra

[DorianKauffmann]

Hello Bugra, So great, thank you very much ! Best, Dorian Le mer. 10 janv. 2024 à 21:23, bugraoezdemir ***@***.***> a écrit :

…

Hi @DorianKauffmann <https://github.com/DorianKauffmann> , Thank you for your positive feedback on BatchConvert and the detailed description of the issue. I can replicate the behaviour using several example images in different formats. The channel information of the original dataset is retained in the metadata of the extracted series. It appears that the root cause lies within the Bio-Formats tools that are wrapped by BatchConvert for conversion. I will investigate further to clarify if this behaviour of Bio-Formats is intentional or a potential bug. In the meanwhile I will also try to figure out a temporary fix in BatchConvert. Cheers, Bugra — Reply to this email directly, view it on GitHub <#24 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AUGQ5D5YTLXSEHQAEQ3MXT3YN32E5AVCNFSM6AAAAABBTQLVRCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBVGY3DANRUHA> . You are receiving this because you were mentioned.Message ID: ***@***.***>