Changed workflow for setting the masking value in the skull stripping #171
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… process to either a global custom value for all inputs or the minimum value within the input image
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Pull request overview
Aligns skull-stripping mask application with the defacing workflow by supporting either a global masking/background value or a per-image minimum when filling voxels outside the brain mask.
Changes:
- Added
masking_valuesupport toBrainExtractorand updatedapply_mask()to fill outside-mask voxels using either the provided global value or the per-image minimum. - Updated
HDBetExtractorandSynthStripExtractorconstructors to accept/forwardmasking_value. - Minor whitespace cleanup in the registration package init.
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
brainles_preprocessing/registration/__init__.py |
Removes stray blank lines/whitespace. |
brainles_preprocessing/brain_extraction/brain_extractor.py |
Introduces masking_value state and updates mask application to use background fill logic consistent with defacing. |
brainles_preprocessing/brain_extraction/synthstrip.py |
Adds masking_value parameter to the extractor constructor (currently not applied during extract() output writing). |
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| def __init__( | ||
| self, border: int = 1, masking_value: Optional[Union[int, float]] = None | ||
| ): |
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masking_value is added to the constructor and forwarded to BrainExtractor, but SynthStripExtractor.extract() never uses self.masking_value when writing the masked output (it still hard-codes bg = np.min([0, img_data.min()])). This makes the new parameter ineffective and the docstring misleading. Update extract() to fill background using the same masking_value/per-image-min logic as BrainExtractor.apply_mask (or drop the parameter if not supported).
| # check whether a global masking value was passed, otherwise choose minimum | ||
| if self.masking_value is None: | ||
| current_masking_value = np.min(input_data) | ||
| else: | ||
| current_masking_value = ( | ||
| np.array(self.masking_value).astype(input_data.dtype).item() | ||
| ) | ||
| # Apply mask (element-wise either input or masking value) | ||
| masked_data = np.where( | ||
| mask_data.astype(bool), input_data, current_masking_value | ||
| ) |
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apply_mask() now supports a global masking_value and falls back to per-image minimum when unset, but the existing unit test only asserts that an output file is created. Please extend tests to assert that voxels outside the mask are set to the expected value for both cases (default min-per-image and a custom masking_value).
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
neuronflow
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neuronflow
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Do we need changes here?
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From my understanding, updating the synthstrip init is still necessary, given that while the center modality might be handled without the apply_mask function in the preprocessor workflow (as seen here)
the moving modalities are then afterwards still handled by the apply_mask function:
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This change implements the the same process as in the defacing step to either use a global custom value for all inputs or the minimum value within the input image as the background value when masking an inage