16 papers · ranked by Valyu relevance
Tomas Vicar, Jan Balvan, Josef Jaros, Florian Jug + 3 more
'Michal Masarik' 'Jaromir Gumulec'] Background Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of…
Diego Martí-Pérez, Valery Naranjo, Adrián Colomer, Ebrahim Karami
Cell segmentation plays a key role in a wide range of biomedical imaging applications, from single-cell analysis to pathology assessment. While classical deep learning architectures such as U-Net, StarDist, and HoVer-Net have set strong baselines, their reliance on domain-specific training limits generalization across…
Andong Wang, Qi Zhang, Yang Han, Sean Megason + 4 more
'Kishore R. Mosaliganti' 'Jacqueline C. K. Lam' 'Victor O. K. Li'] Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell…
Felix Y. Zhou, Zach Marin, Clarence Yapp, Qiongjing Zou + 15 more
'Benjamin A. Nanes' 'Stephan Daetwyler' 'Andrew R. Jamieson' 'Md Torikul Islam' 'Edward Jenkins' 'Gabriel M. Gihana' 'Jinlong Lin' 'Hazel M. Borges' 'Bo-Jui Chang' 'Andrew Weems' 'Sean J. Morrison' 'Peter K. Sorger' 'Reto Fiolka' 'Kevin M. Dean' 'Gaudenz Danuser'] Cell segmentation is the foundation of a wide range of…
Felix Y. Zhou, Zach Marin, Clarence Yapp, Qiongjing Zou + 15 more
Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized two-dimensional (2D) cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and…
Erlend Hodneland, Tanja Kögel, Dominik Michael Frei, Hans-Hermann Gerdes + 1 more
The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to…
Sharif Chowdhury, Meenakshisundaram Kandhavelu, Olli Yli-Harja, Andre S Ribeiro
'Andre S Ribeiro'] Background Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods.…
Seth Winfree
Advanced image analysis with machine and deep learning has improved cell segmentation and classification for novel insights into biological mechanisms. These approaches have been used for the analysis of cells in situ, within tissue, and confirmed existing and uncovered new models of cellular microenvironments in human…
Joe Chalfoun, Michael Majurski, Alden Dima, Christina Stuelten + 2 more
'Adele Peskin' 'Mary Brady'] Background Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting…
Can Fahrettin Koyuncu, Salim Arslan, Irem Durmaz, Rengul Cetin-Atalay + 2 more
'Rengul Cetin-Atalay' 'Cigdem Gunduz-Demir' 'Konradin Metze'] Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand…
Mariusz Marzec, Adam Piórkowski, Arkadiusz Gertych
Background High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in…
Mahmoud Abdolhoseini, Murielle G. Kluge, Frederick R. Walker, Sarah J. Johnson
'Sarah J. Johnson'] Automated cell nucleus segmentation is the key to gain further insight into cell features and functionality which support computer-aided pathology in early diagnosis of diseases such as breast cancer and brain tumour. Despite considerable advances in automated segmentation, it still remains a…
Anubha Gupta, Pramit Mallick, Ojaswa Sharma, Ritu Gupta + 2 more
'Rahul Duggal' 'Yuanquan Wang'] Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool…
Peter Bajcsy, Antonio Cardone, Joe Chalfoun, Michael Halter + 8 more
Background The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image…
Lei Chen, Jianhua Zhang, Shengyong Chen, Yao Lin + 2 more
'Jianwei Zhang'] Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous…
Yang Song, Weidong Cai, Heng Huang, Yue Wang + 2 more
'Mei Chen'] Background Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes.…