FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

Abstract

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM) based label propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse to fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM).One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail.We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0. 81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS +LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.

P2‐231: Systematic Comparison of Different Techniques to Measure Hippocampal Subfield Volumes in ADNI2

“…Brief method summary: Hippocampal surfaces were generated from T1-weighted images of all subjects using multi-atlas FS-LDDMM (Christensen et al, 2015;Khan et al, 2008;Khan et al, 2013;Wang et al, 2009). ADNI2 EMCI and control subjects that had not been selected for this analysis were used to define EMCI-related surface signature labels.…”

“…Of the currently available T1-based approaches, the Bayesian inference labeling as implemented in Freesurfer 5.1. (Van Leemput et al, 2009) and shape analysis based on Large Deformation Diffeomorphic Metric Mapping (Khan et al, 2008) were selected for this project. The former because the algorithm is publicly available and is frequently used, and the second because it was one of the earliest approaches for subfield volumetry that has been continuously refined and optimized for 3 T images.…”

Manually segmented template library for 8-year-old pediatric brain MRI data with 16 subcortical structures

“…Among the current state-of-the-art methods for automated MRI segmentation, registration-based segmentation methods utilize registration as an intermediate step to transfer the ground truth template labels onto the unlabeled target subject images. [1][2][3] These transferred labels from multiple templates are then fused to obtain the final segmentation. Therefore, a library with individual templates and their anatomical labels forms an integral component of such algorithms.…”

Two‐stage multishape segmentation of brain structures using image intensity, tissue type, and location informationa)

“…7 Recent methods benefit from a priori knowledge about the structures of interest. [8][9][10][11] This makes the segmentation process robust to the imperfect image conditions. For the methods developed based on the a priori information, a registration process is essential to integrate the prior model into the segmentation process.…”

“…There is another category of segmentation methods in literature that has used prior knowledge-based terms in the energy function to limit shape variation and gain flexibility. 9,10 However, these methods are sensitive to the weights used for different energy terms. If the weights of the shape constraints, extracted from the training datasets to limit the shape variation around a mean shape, are large, there will be almost no flexibility.…”