Area filter for binary masks #1673
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@@ Coverage Diff @@
## master #1673 +/- ##
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+ Coverage 90.1% 90.14% +0.03%
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Files 231 233 +2
Lines 8835 8870 +35
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+ Hits 7961 7996 +35
Misses 874 874
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shanaxel42
approved these changes
Dec 3, 2019
This filter allows us to filter a BinaryMaskCollection based on the size of the masks. Test plan: Added some basic test cases.
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shanaxel42
pushed a commit
that referenced
this pull request
Dec 4, 2019
This filter allows us to filter a BinaryMaskCollection based on the size of the masks. Test plan: Added some basic test cases.
ttung
pushed a commit
that referenced
this pull request
Dec 5, 2019
Uses the labeling algorithms provided by #1680 and the area filter from #1673 to implement labeling. Depends on #1671, #1673, #1680 Test plan: ISS notebook yields 96 cells. The previous implementation did not support 3D and flattened everything along the Z axis. Processing in 3D exposed issues in `peak_local_max`. If we use the footprint + exclude borders approach, there is an off-by-one error in trimming the Z axis, resulting in completely blank images and no peaks. Therefore, we have to exclude the borders. Because of that, we detect more cells.
mattcai
pushed a commit
that referenced
this pull request
Dec 19, 2019
Uses the labeling algorithms provided by #1680 and the area filter from #1673 to implement labeling. Depends on #1671, #1673, #1680 Test plan: ISS notebook yields 96 cells. The previous implementation did not support 3D and flattened everything along the Z axis. Processing in 3D exposed issues in `peak_local_max`. If we use the footprint + exclude borders approach, there is an off-by-one error in trimming the Z axis, resulting in completely blank images and no peaks. Therefore, we have to exclude the borders. Because of that, we detect more cells.
ttung
pushed a commit
that referenced
this pull request
Jan 13, 2020
Uses the labeling algorithms provided by #1680 and the area filter from #1673 to implement labeling. Depends on #1671, #1673, #1680 Test plan: ISS notebook yields 96 cells. The previous implementation did not support 3D and flattened everything along the Z axis. Processing in 3D exposed issues in `peak_local_max`. If we use the footprint + exclude borders approach, there is an off-by-one error in trimming the Z axis, resulting in completely blank images and no peaks. Therefore, we have to exclude the borders. Because of that, we detect more cells.
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This filter allows us to filter a BinaryMaskCollection based on the size of the masks.
Test plan: Added some basic test cases.