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Sharmishtaa Seshamani

Researcher at Allen Institute for Brain Science

Publications -  41
Citations -  1158

Sharmishtaa Seshamani is an academic researcher from Allen Institute for Brain Science. The author has contributed to research in topics: Biology & Iterative reconstruction. The author has an hindex of 12, co-authored 36 publications receiving 777 citations. Previous affiliations of Sharmishtaa Seshamani include Johns Hopkins University & Washington University in St. Louis.

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Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy.

TL;DR: A label-free method for predicting three-dimensional fluorescence directly from transmitted-light images is presented and it is demonstrated that it can be used to generate multi-structure, integrated images.
Posted ContentDOI

Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy

TL;DR: A label-free method for predicting 3D fluorescence directly from transmitted light images is presented and it is demonstrated that it can be used to generate multi-structure, integrated images.
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A Unified Approach to Diffusion Direction Sensitive Slice Registration and 3-D DTI Reconstruction From Moving Fetal Brain Anatomy

TL;DR: This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain, showing improved rendition of cortical anatomy and extraction of white matter tracts.
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Single-cell transcriptomic evidence for dense intracortical neuropeptide networks

TL;DR: Here, neuron-type-specific patterns of NP gene expression are used to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.
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Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images

TL;DR: This work is the first study to systematically explore supervised classification for CD lesions, a classifier cascade to classify discrete lesions, as well as quantitative assessment of lesion severity, using a well-developed database of 47 studies.