HenriquesLab / HenriquesLab/ZeroCostDL4Mic
StarDist_3D image artifact issue
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Description
Hi,
I am using the StarDist_3D colab notebook and have encountered issues with introduction of image artifacts which result in errors in the segmentation.
Specifically, these manifest as vertical lines in the image which result in errors in segmentation. Confusingly, these vertical features appear in the Raw image that is displayed after running Section 6.1 - see below. These are definitely not present in the raw input files that are accessed by the notebook, also see below.
These artifacts appear to be the result of some sort of alignment, tiling or sub-sampling (??) but looking through the code in Section 6 I do not understand where this could originate from (especially in case of the raw image which should just be loaded as-is from the drive folder?). Is this something to do with the normalisation that is applied? The problem with this is that it does affect the output with lots of apparent features wrongly identified along these vertical lines (see predicted label image). This behaviour seems to be independent of the tiling settings in section 6. Due to the size of the original images (1660x1676 and 30 z slices), some tiling in x-y is necessary, but the position of the artifacts seems largely unaffected by the number of tiles used. And again, it seems surprising that the tiling feature would affect that display of the raw image at the end of section 6.1?
The rest of the segmentation looks quite promising so it would be great to get this to work - any help much appreciated!
Slice from the original stack (matched to output below):

Section 6.1 output view:

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Research direction
Open the StarDist_3D Colab notebook and reproduce the report through Section 6.1 with the supplied raw stack, comparing the original slice and displayed raw image while varying the Section 6 tiling settings. Trace whether normalization, alignment, tiling, or sub-sampling introduces the vertical lines; done means the Section 6.1 raw view matches the input and segmentation no longer follows those artifacts.
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Assessment
- Tech stack
- jupyter-notebook
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100