Ensure compatibility and stability by specifying the minimum version requirement for the `vt` package in both `timagetk_dev.yaml` and `timagetk.yaml`.

`lineaging.py` : Update v2 transformation application based on direction flag. This need to be continued (landmarks, trsfs) !

`lineaging.py` : Update transformation application based on direction flag. This need to be continued (landmarks, trsfs) !

Updated the import path for the `shared_data` module from `timagetk.io.util` to `timagetk.io.dataset`.

Replace print statements with logging, refactor imports & fix issues related to cell_properties changes

Changed all print statements to log statements from the logging module to improve consistency and control over the output. Removed import statements for the logging module in favor of using a centralized logger from the 'timagetk.bin.logger' module. Also included minor fixes.

Split test classes for better organization and readability.
Added new tests covering different data types and dimensions for both `Image`/`vtImage` to `SpatialImage` conversions and vice versa.
Adjusted voxel size initialization for 2D tests and refactored existing test names for clarity.

Replaced all instances of `vtImage` with `Image` in the `test_io_image.py` file to align with the new class naming convention. Updated function calls and variable initializations to ensure consistency across the test file.

Updated the code to use the `Image` class from `vt.image` instead of the `vtImage` class.
Updated assertions and conversion methods accordingly for improved clarity and maintainability.

Refactor method `to_vtimage` to `to_vt_image` in test files and adjust imports to use `vt.image.Image` instead of `timagetk.third_party.vt_image.vtImage`.
This change ensures compatibility with the updated `vt.image` module.

Renamed `to_vtimage` method to `to_vt_image` for consistency and clarity.
Replace `vt.vtImage` by `ct.image.Image` imports, and added utility functions for better error handling.

Adjusted versions for 'vt' and added 'colorlog', 'pyvista', and 'requests' to enhance compatibility and resolve potential issues.
Removed some commented-out legacy dependencies for cleaner and more maintainable code.

Previously, the code did not properly process the case where only the background cell ID was given. This update ensures that if a single background cell ID is passed, the function correctly returns its neighbors.

This will remove CTRL as a dependencies from timagetl.

Streamline the initialization of cell properties to ensure proper voxel size handling and remove redundant code.
Added methods `_init_vt_ppty` and `check_properties` to manage the condition of images for `CellProperties`, enhancing modularity and clarity.

Replaces the deprecated `vtCellProperties` with `CellProperties` across various feature extraction functions for improved efficiency and clarity.
Minor adjustments to some method calls and a few fixes to handle label-specific conditions properly.

Updated the JSON parser to account for optional 'cell_vertex' and 'cell_edge' CSV files. Now, if these files are not specified, empty strings are assigned as defaults. The program then checks their existence before adding them to the list of files to be returned.

Modified the import path for `get_barycenter` and `get_volume` functions to `timagetk.third_party.vt_features` to ensure code consistency and proper module usage.

Modified the `parse_time_index` function to accept both intensity and segmented image dictionaries.

Moved the intensity image, channel names, and time index parsing logic into dedicated functions to improve code readability and maintainability.
This refactoring also includes minor fixes and logging enhancements, making the main function cleaner and more focused on high-level processing.

Replaced `argparse` with `click` for command-line argument parsing in the `multicut_segmentation.py`. Updated entry point in `pyproject.toml` to reflect the change from `run` to `main`.
 This improves argument validation and user experience.

Introduced a new --ref-idx command-line option to specify reference image indices for multi-angle images.
Updated the main function to handle the newly added option and ensure correct indexing of reference images.
This enhancement improves flexibility when working with multi-angle image datasets.

Revised the intensity image dictionary parsing in `unet_predict.py` and `multicut_segmentation.py` to support multi-angle images by using the first image as a reference.
Added logging to confirm the selected reference image for each time point, ensuring more accurate and clear operational details.

Removed unused imports from `multicut_segmentation.py` to clean up the code.

Added recommendations for input images, and updated several click options to show default values in the help text while reorganizing import statements.
Improved processing.json to include more metadata for better tracking of the prediction process.

Check the given patch size comment has been corrected for grammar.
Additionally, removed an unnecessary condition that sliced the prediction image based on the original image dimensions.

Replaced `imread` import with `pims_read` to align with new image reading API.
 Added support for reading channel names from JSON and updated channel-related logging.
 Corrected option naming for Click options to use hyphens instead of underscores.

Updated the logging of voxel-sizes for mean and fused images to display the values rounded to three decimal places.