Fig. 2: Qualitative evaluations of the initial segmentation and post-processing algorithms.
From: Robust automated calcification meshing for personalized cardiovascular biomechanics
a Assessing the segmentation accuracy in two image slice views from two test-set patients. GT ground-truth segmentation, DL deep learning segmentation using “GDL (ours)”, post: result of PostProcessCa2Seg using either GT or DL as the initial segmentation, final: result of the full C-MAC. Yellow: calcification, red: partial LV myocardium, blue: aorta, (green, orange, purple): aortic valve leaflets. b Before and after PostProcessCa2Seg on one test-set patient, where the white dashed circles highlight the regions with clear gap-closing effects on the voxelgrid segmentation. c Visualizing the effects of PostProcessCa2Seg on the final C-MAC mesh. Purple dots indicate the merged nodes between the calcification and the leaflet mesh, and the black dashed circles indicate the regions with large changes in the merged nodes. This illustrates both the benefit and drawback of our post-processing algorithm. Benefit: improved anatomical consistency with the surrounding tissue. Drawback: some overestimation of calcified regions.
