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Shravani Deo

dblp:370/5145 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 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
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › bioimage informatics › bioimage analysis
microscopy image analysis
0.712023
Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks · Bioinform. 2023

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

resnet50 · 0.7image segmentation · 0.7convolutional neural network · 0.7
YearPublicationVenuePosition
2023 Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks
abstract
MOTIVATION: Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner. RESULTS: Our trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining. AVAILABILITY AND IMPLEMENTATION: Open-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.
Kwan-Ling Wu, Melisa A. Martinez-Paniagua, Kate Reichel, Prashant Menon, Shravani Deo, Badrinath Roysam, Navin Varadarajan
Bioinform.5