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. Author manuscript; available in PMC: 2020 Oct 19.
Published in final edited form as: Med Image Anal. 2018 May 22;48:95–106. doi: 10.1016/j.media.2018.05.008

Table 3.

Segmentation accuracy on the 2017 MICCIA ACDC dataset. Segmentation accuracy is reported as Dice coefficient in the ACDC challenge, but as IoU elsewhere in this work; therefore, both are reported here. (Note that Dice = 2 × IoU/(1 + IoU)). Results are reported for the Network B variant of the Ω-Net; for the results by Isensee et al. (2017) published in STACOM; and for the same group’s unpublished arxiv.org revision Isensee et al. (2018). Boldface formatting indicates the best performing model for each foreground class. MICCAI: Medical Image Computing and Computer Assisted Intervention Society; ACDC: Automated Cardiac Diagnosis Challenge.

Structure LV Bloodpool RV Bloodpool LV Myocardium
Jaccard index (IoU)
Ω-Net 0.912 0.852 0.803
Isensee et al. (2018) 0.896 0.832 0.826
Isensee et al. (2017) 0.869 0.784 0.775
Dice coefficient
Ω-Net 0.954 0.920 0.891
Isensee et al. (2018) 0.945 0.908 0.905
Isensee et al. (2017) 0.930 0.879 0.873