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. Author manuscript; available in PMC: 2022 Aug 4.
Published in final edited form as: Annu Rev Biomed Eng. 2020 Mar 13;22:127–153. doi: 10.1146/annurev-bioeng-060418-052147

Table 3.

Median Dice overlaps for 202 ADNI subjects using different approachesa

Method Left hippocampus Right hippocampus Whole hippocampus Time
Patch-based 0.848 0.842 0.844 10 min
SRCb 0.873 0.869 0.871 40 min
DDLSb 0.872 0.872 0.872 3–6 min
F-DDLSb 0.865 0.859 0.864 <1 min
a

The numbers in bold represent the highest Dice overlaps among different methods. All three sparse coding methods (SRC, DDLS, and F-DDLS) outperform standard nonlocal patch-based classification. The sparse dictionary learning methods are also computationally efficient compared with conventional, nonsparse patch-based segmentation. Bold entries indicate the best-performing method.

b

Methodology utilizing sparsity.

Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; DDLS, discriminative dictionary learning for segmentation; F-DDLS, fixed DDLS; SRC, sparse representation classification.