Table 2.

Methods for automatic hippocampal segmentation

Challenge	Approach and results	Reference
Hippocampal segmentation
Multi-atlas segmentation	Presents a unified algorithm, Hippocampal Unified Multi-Atlas Networks (HUMAN), that combines the accuracy of multi-atlas approaches with artificial neural network classification. The algorithm was robust and accurate compared to manual segmentation.	[65]
	Proposes a learning-based atlas selection method that learns the relationship between each pair of atlases and target images. The method improved segmentation accuracy over other widely used multi-atlas segmentation methods.	[66]
	Present a novel segmentation method that uses a manifold learning technique to obtain spatially local weights for atlas label fusion. The weights depend on all pairwise similarities of the population. Segmentation using this method was highly correlated with manual segmentation.	[67]
	Propose a graphical approach to labeling using a Markov random field formulation which constructs graphs connecting atlases and the target image. This unified framework allows more efficient label propagation. The method was robust and accurate.	[68]
	Present an algorithm Multiple Automatically Generated Templates (MAGeT-Brain) which propagates atlas segmentation is to template library. These are then propagated to each target image and fused using a label fusion method. The method was compared with existing methods including FIRST and FreeSurfer. MAGeT-Brain achieved a higher Dice’s Similarity Coefficient with manual segmentation volumes than produced by FreeSurfer and FIRST.	[69]
Use of hippocampal shape information	Uses spectral Laplace-Beltrami wavelets to obtain high-resolution hippocampal shaped deformations. This resulted in a sensitivity of 96% and a specificity of 90% in the classification of AD versus NC using hippocampal shapes.	[70]
	Present a method using linear registration of brain images to a standard template, feature extraction, and voxel classification using a random Forest algorithm to determine whether voxels belong to the hippocampus or not. Outperformed FreeSurfer.	[71]
	Constructed a high-resolution atlas from manually segmented hippocampal substructures which included manual annotations for neighboring structures. The atlas, released as part of FreeSurfer (version 6.0), outperforms the atlas and FreeSurfer version 5.3.	[72]
	Combined that the use of FreeServer, FIRST, and SPHARM software packages to create an atlas by mapping interpolated subfields automation onto the average surface. Atlas has good reproducibility using ADNI data.	[73]
Automated hippocampal segmentation for clinical use	Uses a fully automated multi-atlas segmentation. Found a high Dice Similarity Coefficient with manual segmentation. Suggests that NeuroReader could have clinical applications.	[74]
	Present a fully automated pipeline using an affine registration step and classification of voxels using a Random Forest classifier. Classification was performed slice by slice along each of three orthogonal directions and achieved comparable results to manual segmentation.	[75]
	Present a fully automated pipeline which is atlas based and uses Statistical Parametric Mapping (SPM) software. The automated pipeline was computationally inexpensive, accurate, and is freely available as an SPM8 toolbox.	[76]
Development of a robust hippocampal atrophy biomarker	Describe development of a longitudinal hippocampal atrophy biomarker which is not confounded by factors such as acquisition noise or artifacts, and physiological variations. Biomarker detects hippocampal atrophy due to disease and not to other factors such as long-term aging. In combination with baseline volumes, the method was highly accurate in discriminating patient groups.	[77]
Patch label fusion	Proposed a novel patch-based labels fusion method that combines the two approaches via matrix completion. The method results in more accurate segmentation than either with the reconstruction-based or the classification-based approaches.	[78]
	Present a novel patch-based label fusion framework that uses an optimized PatchMatch Label Fusion (OPAL) strategy. OPAL produced a segmentation accuracy highly correlated with manual segmentation.	[79]
	Introduce three new label fusion contributions: (1) the feature representation for each image patch encodes local information; (2) each atlas image patch is further partitioned into partial image patches; (3) label fusion is improved with a hierarchical approach. The improvements proposed resulted in improved accuracy of segmentation.	[80]

Abbreviations: AD, Alzheimer’s disease; NC, normal cognition; ADNI, Alzheimer’s Disease Neuroimaging Initiative.