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Monday, September 26, 2016
Thursday, September 22, 2016
Tuesday, September 20, 2016
Whole Breast segmentation: one idea is to forgo the Chest Wall Segmentation (CWS) and go directly to Whole Breast Segmentation.
One very quick prototype is based in the old algorithm principally based on the pure morphology. Of course there are many details to figure out.
Case is "DASS T1-nonFS"
![]() |
| Selected slices for the WBS |
Whole Breast segmentation: one idea is to forgo the Chest Wall Segmentation (CWS) and go directly to Whole Breast Segmentation.
One very quick prototype is based in the old algorithm principally based on the pure morphology. Of course there are many details to figure out.
Case is "DASS T1-nonFS"
![]() |
| Selected slices for the WBS |
Friday, September 16, 2016
Thursday, September 15, 2016
Wednesday, September 14, 2016
Tuesday, September 13, 2016
EdgeDetector 3D: greatly reduced the memory requirements.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradient Map which is extremely memory consuming.
Also, this function is applicable throughout FireVoxel where only the Edge Magnitude map or Binary Edge are sufficient.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradient Map which is extremely memory consuming.
Also, this function is applicable throughout FireVoxel where only the Edge Magnitude map or Binary Edge are sufficient.
EdgeDetector 3D: greatly reduced the memory requirements.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradien Map which is extremely memory consuming.
On BPD_N01 test dataset (from BiCal sample set), the Max Used memory was reduced about x10 times from 750MB down to 73 MB. This was done by introducing a specialized simplified function for most common scenarios. This simplified function avoids generation of the Gradien Map which is extremely memory consuming.
Sunday, September 11, 2016
Thursday, September 8, 2016
Saturday, September 3, 2016
Friday, September 2, 2016
Thursday, September 1, 2016
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