25 papers · ranked by Valyu relevance
Michael Y. Lee, Jacob S. Bedia, Salil S. Bhate, Graham L. Barlow + 4 more
Background Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the…
Elham Karimi, Morteza Rezanejad, Benoit Fiset, Lucas Perus + 8 more
High-parameter multiplex immunostaining techniques have revolutionized our ability to image healthy and diseased tissues with unprecedented depth; however, accurate cell identification and segmentation remain significant downstream challenges. Identifying individual cells with high precision is a requisite to reliably…
Guochang Ye, Mehmet Kaya, Larbi Boubchir, Charles Tijus + 3 more
Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segmentation method involving morphological operations to automatically…
Takanobu A Katoh, Yohsuke T Fukai, Tomoki Ishibashi
Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to…
Yuxing Wang, Junhan Zhao, Hongye Xu, Cheng Han + 5 more
Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform…
Liang, Peixian, Ding, Yifan + 20 more
— State-of-the-art (SOTA) methods for cell instance segmentation are based on deep learning (DL) semantic segmentation approaches, focusing on distinguishing foreground pixels from background pixels. In order to identify cell instances from foreground pixels (e.g., pixel clustering), most methods decompose instance…
Bogdan Kochetov, Phoenix Bell, Paulo S. Garcia, Akram S. Shalaby + 12 more
'Rebecca Raphael' 'Benjamin Raymond' 'Brian J. Leibowitz' 'Karen Schoedel' 'Rhonda M. Brand' 'Randall E. Brand' 'Jian Yu' 'Lin Zhang' 'Brenda Diergaarde' 'Robert E. Schoen' 'Aatur Singhi' 'Shikhar Uttam'] Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at…
Uriah Israel, Markus Marks, Rohit Dilip, Qilin Li + 10 more
'Elora Pradhan' 'Edward Pao' 'Shenyi Li' 'Alexander Pearson-Goulart' 'Pietro Perona' 'Georgia Gkioxari' 'Ross Barnowski' 'Yisong Yue' 'David Van Valen'] Cells are the fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging…
Xiaohang Fu, Yingxin Lin, David M. Lin, Daniel Mechtersheimer + 6 more
'Chuhan Wang' 'Farhan Ameen' 'Shila Ghazanfar' 'Ellis Patrick' 'Jinman Kim' 'Jean Y. H. Yang'] Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and…
Felix Y. Zhou, Clarence Yapp, Zhiguo Shang, Stephan Daetwyler + 14 more
Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by…
Giuseppe Giacopelli, Michele Migliore, Domenico Tegolo
Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A…
Xiaohang Fu, Yingxin Lin, David M Lin, Daniel Mechtersheimer + 6 more
Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to…
Nikomidisz Jorgosz Eftimiu, Michal Kozubek
Automatic detection and tracking of cells in microscopy images are major applications of computer vision technologies in both biomedical research and clinical practice. Though machine learning methods are increasingly common in these fields, classical algorithms still offer significant advantages for both tasks…
Axel Andersson, Andrea Beháňová, Carolina Wählby, Filip Malmberg
> Abstract. The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which…
Hafizi Malik, Ahmad Syahrin Idris, Siti Fauziah Toha, Izyan Mohd Idris + 3 more
Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell…
Laura Wiggins, Peter J. O’Toole, William J. Brackenbury, Julie Wilson
Segmentation and tracking are essential preliminary steps in the analysis of almost all live cell imaging applications. Although the number of open-source software systems that facilitate automated segmentation and tracking continue to evolve, many researchers continue to opt for manual alternatives for samples that…
Valentina Vadori, Jean‐Marie Graïc, Antonella Peruffo, Giulia Vadori + 2 more
method for histo(patho)logical image Analyses and a new, open, Nissl-stained dataset for brain cytoarchitecture studies Authors: ['Valentina Vadori' 'Jean‐Marie Graïc' 'Antonella Peruffo' 'Giulia Vadori' 'Livio Finos' 'Enrico Grisan'] Delineating and classifying individual cells in microscopy tissue images is a complex…
Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen + 8 more
'Yaofang Liu' 'Adnan Shakoor' 'Han Zhao' 'Beijia Lu' 'Shaohua Zhi' 'Raymond C. Chan' 'Dong Sun'] Despite recent advances in data-independent and deep-learning algorithms, unstained live adherent cell instance segmentation remains a long-standing challenge in cell image processing. Adherent cells' inherent visual…
Balint Beres, Kinga Dora Kovacs, Nicolett Kanyo, Beatrix Peter + 2 more
Cancer Classification from the Spatial Distribution of Adhesion Contact Kinetics Authors: ['Balint Beres' 'Kinga Dora Kovacs' 'Nicolett Kanyo' 'Beatrix Peter' 'Inna Szekacs' 'Robert Horvath'] There is an increasing need for simple-to-use, noninvasive, and rapid tools to identify and separate various cell types or…
Jingzhou Zhang, Justin Griffin, Koushik Roy, Alexander Hoffmann + 1 more
Measurements of cell lineages is central to a variety of fundamental biological questions, ranging from developmental to cancer biology. However, accurate lineage tracing requires nearly perfect cell tracking, which can be challenging due to cell motion during imaging. Here we demonstrate the integration of…
Authors not listed
Vertex models have been established as powerful tools for the cell-based simulation of epithelial tissues as they allow a detailed description of their mechanical development with respect to the properties of individual cells. Thus, they suit the challenges of simulating intestinal organoids which arise from the…
Kanda Borgognoni, Bradley Guilliams, Rachel Cohen, Christopher Ackerson
A cloneable NanoParticle (cNP) is an inorganic nanoparticle that is synthesized by a protein. The protein determines the elemental composition, size, morphology, and other properties of the nanoparticle. Here, we describe the use of a cloneable Selenium NanoParticle (cSeNP) as a cloneable imaging contrast agent in…
Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen + 3 more
In materials research, the task of characterizing hundreds of different materials traditionally requires equally many human hours spent measuring samples one by one. We demonstrate that with the integration of computer vision into this material research workflow, many of these tasks can be automated, significantly…
Authors not listed
Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) is a powerful technique for molecular analysis of surfaces; however, its application of single cell studies has not been previously published. In the current work, a commercial DESI setup (DESI XS) coupled to a mass spectrometer was used to analyze…
Authors not listed
The Single-probe is a multifunctional device that can be coupled to mass spectrometry (MS) for molecular analysis of microscale samples, such as single cells, tissue slices, and multicellular spheroids, under ambient conditions. In Single-probe single cell MS (SCMS) studies, this technique leverages direct sampling and…