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Sai Srikanth Lakkimsetty

dblp:426/4581 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 56% Bioinformatics and computational biology · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metabolomics
mass spectrometry imaging
0.812024
<tt>MSIreg</tt>: an R package for unsupervised coregistration of mass spectrometry and H&E images · Bioinform. 2024
Medical and health informatics › medical imaging
medical image analysis
0.812024
<tt>MSIreg</tt>: an R package for unsupervised coregistration of mass spectrometry and H&E images · Bioinform. 2024
Medical and health informatics
digital pathology
0.212024
<tt>MSIreg</tt>: an R package for unsupervised coregistration of mass spectrometry and H&E images · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

landmark-free registration · 0.8elastic deformation modeling · 0.8
YearPublicationVenuePosition
2024 <tt>MSIreg</tt>: an R package for unsupervised coregistration of mass spectrometry and H&E images
abstract
SUMMARY: Joint analysis of mass spectrometry images (MS images) and microscopy images of hematoxylin and eosin (H&E) stained tissues assists pathologists in characterizing the morphological structure of the tissues, and in performing diagnosis. Unfortunately, the analysis is undermined by substantial differences between these modalities in terms of aspect ratios, spatial resolution, number of channels in each image, as well as by large global or small local elastic spatial deformations of one image with respect to the other. Therefore, accurate coregistration of the images is a critical pre-requisite for their joint interpretation. We introduce MSIreg, an open-source R package for coregistration of MSI and H&E images. MSIreg is designed for high-dimensional MSI experiments where each spatial location is represented by thousands of mass features. Unlike most existing coregistration methods, MSIreg implements a landmark free workflow, and quantitative metrics for performance evaluation. We evaluate the performance of MSIreg on six case studies, including coregistration of contiguous tissues with large deformations, as well as simultaneous coregistration of 29 tissue microarray cores. AVAILABILITY AND IMPLEMENTATION: The R package, installation instructions, and fully reproducible vignettes describing methods and Case Studies are available open-source under the GPL-3.0 license at https://github.com/sslakkimsetty/msireg/.
Sai Srikanth Lakkimsetty, Kylie A. Bemis, Verena Stehl, Peter Bronsert, Melanie Christine Föll, Olga Vitek
Bioinform.1