OME-TIFF

Prerequisites

Before starting this lesson, you should be familiar with:

Learning Objectives

After completing this lesson, learners should be able to:

  • Understand the basic concept of the TIFF image data format

  • Understand that there are various TIFF variants, such as ImageJ-TIFF and OME-TIFF

  • Create OME-TIFF files

  • Convert other image data into OME-TIFF files

  • Inspect OME-TIFF image data and metadata

Motivation

Concept map

graph TD T("TIFF") --- I("Image data and metadata") T --- P("Planar image data model") T --- V("Variants exist") V --- OT("OME-TIFF is an important variant")



Figure


TIFF is a planar file format, where one TIFF file can contain multiple image planes of different sizes and different data types, which are specified in the respective IFD block. OME-TIFF features an additional OME-XML metadata block, which describes additional important microscopy metadata such as the mapping of IFDs to channel, time-point, and z-plane. In addition, OME-TIFF supports storing different image series within one TIFF file. Within an images series the image datatype, width and height are the same for all planes. In the figure, the OME-TIFF contains two image series, where the first is a z-stack and the second is a multi-channel image.



General comments

The TIFF file format is already complex and the OME-TIFF variant is adding even more metadata on top. To fully understand everything is way beyond the aim of this training material. We will just scratch the surface to understand the most important concepts.

IFDs and image planes

TIFF is a planar image data format. Internally there are always pairs of IFD and data blocks. IFD stands for “Image File Directory” and contains information about where the corresponding image data block can be found in the file, the image width and height, as well as its datatype.

One TIFF file can contain many IFD/data pairs.

The IFD/data pairs may contain images of different sizes and different datatypes.

Physical pixel size

The TIFF format does natively support storing pixel size metadata, typically in units of centimetre or inches.

Resolution pyramids and chunking

The TIFF format supports the concept of resolution pyramids, and chunked storage of pixels within one plane. However there is no 3-D chunking available. Thus, TIFF can in fact be used as a 2-D big image data format.

Channels, z-planes, and time points

The TIFF format does not natively support storing information about which channel, z-plane or time-point an IFD/data pair belongs to. This bioimaging specific information is handled by additional metadata of TIFF variants, such as OME-TIFF.




Activities

Open OME-TIFF


Show activity for:  

Fiji Bio-Formats

  • Open Fiji
  • Drag and drop the OME-TIFF image onto Fiji
  • The Bio-Formats Importer will open
    • [X] Display OME-XML metadata
    • [ OK ]
    • Observe that there are only two datasets, but stored at different resolution levels
    • Select all images
    • [ OK ]
  • Inspect the OME-XML metadata

Python/BioIO

# %% 
# Open a ome.tiff image file
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
# Note: for only dealing with ome.tif just do pip install bioio bioio-ome-tiff


# %%
# Load .ome.tiff file
# Please download the image from 'https://github.com/NEUBIAS/training-resources/blob/master/image_data/xy_xyc__two_images.ome.tiff'
# save image in your current directory in a folder called ExampleImages
# - Observe that BioImage chooses the correct reader plugin
from bioio import BioImage
from pathlib import Path
bioimage = BioImage(Path().cwd()/'ExampleImages/xy_xyc__two_images.ome.tiff')
print(bioimage)
print(type(bioimage))

# %%
# Inspect the number of images in the file
print(f'Number of Images: {bioimage.scenes}')

# %%
# Inspect dimension and shape of the first image
bioimage.set_scene(0)
print(f'Image dimension: {bioimage.dims}')
print(f'Dimension order is: {bioimage.dims.order}')
print(f'Image shape: {bioimage.shape}')

# %%
# Extract image data (5D)
image_data = bioimage.data
print(f'Image type: {type(image_data)}')
print(f'Image array shape: {image_data.shape}')
# Extract specific image part
image_data = bioimage.get_image_data('YX',C=1)
print(f'Image type: {type(image_data)}')
print(f'Image array shape: {image_data.shape}')

# %%
# Read pixel size
print(f'Pixel size: {bioimage.physical_pixel_sizes}')
# Read metadata
print(f'Metadata type: {type(bioimage.metadata)}')
# print(f'Metadata: {bioimage.metadata}')

# %%
# Inspect dimension and shape of the second image
bioimage.set_scene(1)
print(f'Image dimension: {bioimage.dims}')
print(f'Dimension order is: {bioimage.dims.order}')
print(f'Image shape: {bioimage.shape}')

# %%
# Extract image data (5D)
image_data = bioimage.data
print(f'Image type: {type(image_data)}')
print(f'Image array shape: {image_data.shape}')

# %%
# Read pixel size
print(f'Pixel size: {bioimage.physical_pixel_sizes}')
# Read metadata
print(f'Metadata type: {type(bioimage.metadata)}')
# print(f'Metadata: {bioimage.metadata}')



Save... OME-TIFF

Save image file as OME-TIFF.


Show activity for:  

Python/BioIO

# %% 
# Save a ome.tiff image file
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio  notebook

# %%
# Create a random image to save
import numpy as np

image = np.random.rand(512,512)

# %%
# Save as OME Tiff
from bioio.writers import OmeTiffWriter
from pathlib import Path

OmeTiffWriter.save(data=image, # image to save
                   uri=Path().cwd()/'TestImage.ome.tiff', # path to save to
                   dim_order='YX') # dimension order

# %%
# Save two images in one ome tiff
# generate two images
image0 = np.random.rand(3,512,512)
image1 = np.random.rand(256,256)

# add some pixel size data
from bioio_base.types import PhysicalPixelSizes

pixelsize0 = PhysicalPixelSizes(Z=0.3,Y=0.1,X=0.1)
pixelsize1 = PhysicalPixelSizes(Z=None,Y=0.5,X=0.5)

OmeTiffWriter.save(data=[image0,image1],
                   uri=Path().cwd()/'TwoImages.ome.tiff',
                   dim_order=['ZYX','YX'],
                   physical_pixel_sizes=[pixelsize0,pixelsize1])



Convert an image file to OME-TIFF


Show activity for:  

NGFF-Converter

This activity uses the NGFF Converter to convert a LIF image data set into an OME-TIFF image data set; and it uses Fiji to inspect the content of both the LIF and the OME-TIFF.

  • Inspect LIF using Fiji:
    • Drag and drop the LIF image onto Fiji
    • The Bio-Formats Importer will open
      • [X] Display OME-XML metadata
      • [ OK ]
      • Select all images
      • [ OK ]
    • Take note of
      • Number of images
      • For each image note its dimensions and spatial calibration
    • Inspect the OME-XML metadata and note down some values that you deem important
  • Convert LIF to OME-TIFF using the NGFF converter:
    • Install the NGFF converter TODO: add link
    • Open the NGFF converter
    • Drag and drop the LIF file onto it
    • A dialog will open:
      • Output format: OME-TIFF
      • Output location: As you wish
      • [ Apply ]
    • [ Run Jobs ]
  • Inspect OME-TIFF using Fiji:
    • Drag and drop the created OME-TIFF onto Fiji
    • Repeat the same steps as for the original LIF file
    • Check whether all image data and metadata has been preserved

Key points

  • The NGFF converter is a nice tool to create OME-TIFF
  • OME-TIFF can contain multiple image data sets
  • OME-TIFF can contain resolution pyramids

Python/BioIO

# %% 
# Open a ome.tiff image file
# minimal conda env for this module
# conda create -n ImageFileFormats python=3.10
# activate ImageFileFormat
# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
# Note: for only dealing with ome.tif just do pip install bioio bioio-ome-tiff

# %%
# Load .lif file
from bioio import BioImage
image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
bioimage = BioImage(image_url)

# %%
# Option 1: Direct conversion
from pathlib import Path
bioimage.save(Path().cwd()/'converted_file.ome.tiff')

# Alternative
BioImage(image_url).save(Path().cwd()/'direct_conversion.ome.tiff')

# %%
# Option 2: Indirect conversion
# Single image to OME Tiff
from bioio.writers import OmeTiffWriter

# Extract image and pixel info
bioimage.set_scene(0)
image1 = bioimage.data.squeeze()
pixel_info1 = bioimage.physical_pixel_sizes

# Perform some modification/analysis/...

# Save as OME Tiff
OmeTiffWriter.save(data=image1, # image to save
                   uri=Path().cwd()/'ConvertSingleImage.ome.tiff', # path to save to
                   physical_pixel_sizes = pixel_info1, 
                   dim_order='CYX') # dimension order

# %%
# Two images
# Extract image and pixel info
bioimage.set_scene(1)
image2 = bioimage.data.squeeze()
pixel_info2 = bioimage.physical_pixel_sizes

# Perform some modification/analysis/...

# Save as OME Tiff
OmeTiffWriter.save(data=[image1,image2],
                   uri=Path().cwd()/'ConvertTwoImages.ome.tiff',
                   dim_order=['CYX','YX'],
                   physical_pixel_sizes=[pixel_info1,pixel_info2])






Assessment

Fill in the blanks

  1. TODO ___ .
  2. TODO ___ .

Solution

  1. TODO
  2. TODO




Follow-up material

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