Sandhitsu R. Das

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Multi-atlas segmentation is an effective approach for automatically labeling objects of interest in biomedical images. In this approach, multiple expert-segmented example images, called atlases, are registered to a target image, and deformed atlas segmentations are combined using label fusion. Among the proposed label fusion strategies, weighted voting with(More)
This paper describes the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu ammonis fields CA1, CA2/3; the dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a(More)
We present and evaluate a new method for automatically labeling the subfields of the hippocampal formation in focal 0.4 × 0.5 × 2.0mm(3) resolution T2-weighted magnetic resonance images that can be acquired in the routine clinical setting with under 5 min scan time. The method combines multi-atlas segmentation, similarity-weighted voting, and a novel(More)
We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper(More)
Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter-white matter interface and the estimated gray matter-cerebrospinal fluid interface is given by a diffeomorphic mapping in(More)
We evaluate a fully automatic technique for labeling hippocampal subfields and cortical subregions in the medial temporal lobe in in vivo 3 Tesla MRI. The method performs segmentation on a T2-weighted MRI scan with 0.4 × 0.4 × 2.0 mm(3) resolution, partial brain coverage, and oblique orientation. Hippocampal subfields, entorhinal cortex, and perirhinal(More)
Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to(More)
The measurement of hippocampal volumes using MRI is a useful in-vivo biomarker for detection and monitoring of early Alzheimer's disease (AD), including during the amnestic mild cognitive impairment (a-MCI) stage. The pathology underlying AD has regionally selective effects within the hippocampus. As such, we predict that hippocampal subfields are more(More)
Diffusion tensor imaging plays a key role in our understanding of white matter (WM) both in normal populations and in populations with brain disorders. Existing techniques focus primarily on using diffusivity-based quantities derived from diffusion tensor as surrogate measures of microstructural tissue properties of WM. In this paper, we describe a novel(More)
Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this assumption significantly reduces the efficiency of multi-atlas(More)