P
Poulami Somanya Ganguly
Researcher at Centrum Wiskunde & Informatica
Publications - 13
Citations - 385
Poulami Somanya Ganguly is an academic researcher from Centrum Wiskunde & Informatica. The author has contributed to research in topics: Iterative reconstruction & Computer science. The author has an hindex of 3, co-authored 11 publications receiving 249 citations. Previous affiliations of Poulami Somanya Ganguly include Francis Crick Institute & Leiden University.
Papers
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Journal ArticleDOI
Vertex models: from cell mechanics to tissue morphogenesis
TL;DR: Various formulations of vertex models that have been proposed for describing tissues in two and three dimensions are reviewed, a generic formulation using a virtual work differential is discussed, and applications of vertices to biological morphogenetic processes are reviewed.
Journal ArticleDOI
Apical and Basal Matrix Remodeling Control Epithelial Morphogenesis
Maria-del-Carmen Diaz-de-la-Loza,Robert P. Ray,Poulami Somanya Ganguly,Silvanus Alt,Silvanus Alt,John Robert Davis,Andreas Hoppe,Nic Tapon,Guillaume Salbreux,Barry J. Thompson +9 more
TL;DR: It is shown that elongation of the wings and legs of Drosophila involves a columnar-to-cuboidal cell shape change that reduces cell height and expands cell width and dynamic changes in actomyosin contractility are induced to drive epithelial morphogenesis in three dimensions.
Journal ArticleDOI
Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.
Johannes Leuschner,Maximilian Schmidt,Poulami Somanya Ganguly,Vladyslav Andriiashen,Sophia Bethany Coban,Alexander Denker,Dominik F. Bauer,Amir Hadjifaradji,Kees Joost Batenburg,Peter Maass,Maureen van Eijnatten +10 more
TL;DR: In this article, the authors present the results of a data challenge that they organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint.
LoDoPaB-CT Challenge Reconstructions compared in "Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications"
Johannes Leuschner,Maximilian Schmidt,Poulami Somanya Ganguly,Vladyslav Andriiashen,Sophia Bethany Coban,Alexander Denker,Maureen van Eijnatten +6 more
Book ChapterDOI
Atomic Super-Resolution Tomography
TL;DR: In this paper, a grid-free discrete tomography algorithm is proposed to recover the atomic positions more accurately for common lattice defects, which allows for continuous deviations of the atom locations similar to super-resolution approaches.