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docs/source/api.rst

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@@ -131,8 +131,8 @@ Segmentation3D
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Methods
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^^^^^^^
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.. automethod:: ndx_microscopy.Segmentation3D.add_roi
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.. automethod:: ndx_microscopy.Segmentation3D.voxel_to_image
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.. automethod:: ndx_microscopy.Segmentation3D.image_to_voxel
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.. automethod:: ndx_microscopy.Segmentation3D.voxel_to_volume
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.. automethod:: ndx_microscopy.Segmentation3D.volume_to_voxel
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.. automethod:: ndx_microscopy.Segmentation3D.create_roi_table_region
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SegmentationContainer

docs/source/examples.rst

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# Add 3D ROIs using image masks
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roi_mask = np.zeros((height, width, depths), dtype=bool)
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roi_mask[256:266, 256:266, 4:6] = True # 10x10x2 ROI
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segmentation.add_roi(image_mask=roi_mask)
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segmentation.add_roi(volume_mask=roi_mask)
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# Add ROIs using voxel masks
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voxel_mask = [
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num_rois = len(segmentation.id)
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responses = np.zeros((frames, num_rois))
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for i, roi_mask in enumerate(segmentation.image_mask[:]):
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for i, roi_mask in enumerate(segmentation.volume_mask[:]):
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roi_data = data[:, roi_mask]
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responses[:, i] = np.mean(roi_data, axis=1)
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# Access ROI data
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ophys = nwbfile.processing['ophys']
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rois = ophys['volume_rois']
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roi_masks = rois.image_mask[:]
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roi_masks = rois.volume_mask[:]
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# Access responses
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responses = ophys['volume_responses']

docs/source/format.rst

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neurodata_type_inc: Segmentation
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doc: Results from image segmentation of a specific volumetric imaging space.
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datasets:
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- name: image_mask
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- name: volume_mask
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neurodata_type_inc: VectorData
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dims:
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- - num_roi

docs/source/release_notes.rst

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*************
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Release Notes
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*************
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Version 0.3.0 (May 29, 2025)
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==============================
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Deprecations and Changes
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-------------------------
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* Changed `image_to_voxel` in `volume_to_voxel`
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* Changed `voxel_to_image` in `voxel_to_volume`
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* Changed `image_mask` in `volume_mask` in VolumetricSegmentation
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Features
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--------
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Improvements
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------------
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Notes
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------
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Version 0.2.0 (March 19, 2025)
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==============================

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