@@ -492,7 +492,7 @@ Example of volumetric imaging with 3D ROI segmentation:
492492 # Add 3D ROIs using image masks
493493 roi_mask = np.zeros((height, width, depths), dtype = bool )
494494 roi_mask[256 :266 , 256 :266 , 4 :6 ] = True # 10x10x2 ROI
495- segmentation.add_roi(image_mask = roi_mask)
495+ segmentation.add_roi(volume_mask = roi_mask)
496496
497497 # Add ROIs using voxel masks
498498 voxel_mask = [
@@ -512,7 +512,7 @@ Example of volumetric imaging with 3D ROI segmentation:
512512 num_rois = len (segmentation.id)
513513 responses = np.zeros((frames, num_rois))
514514
515- for i, roi_mask in enumerate (segmentation.image_mask [:]):
515+ for i, roi_mask in enumerate (segmentation.volume_mask [:]):
516516 roi_data = data[:, roi_mask]
517517 responses[:, i] = np.mean(roi_data, axis = 1 )
518518
@@ -552,7 +552,7 @@ Example of volumetric imaging with 3D ROI segmentation:
552552 # Access ROI data
553553 ophys = nwbfile.processing[' ophys' ]
554554 rois = ophys[' volume_rois' ]
555- roi_masks = rois.image_mask [:]
555+ roi_masks = rois.volume_mask [:]
556556
557557 # Access responses
558558 responses = ophys[' volume_responses' ]
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