22 papers · ranked by Valyu relevance
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…
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…
Menghao Li, Wenquan Feng, Shuchang Lyu, Lijiang Chen + 1 more
Cell instance segmentation is a new and challenging task aiming at joint detection and segmentation of every cell in an image. Recently, many instance segmentation methods have applied in this task. Despite their great success, there still exists two main weaknesses caused by uncertainty of localizing cell center…
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…
Lin Geng Foo, Jiamei Sun, Alexander Binder
We consider the problem of segmenting cell nuclei instances from Hematoxylin and Eosin (H&E) stains with weak supervision. While most recent works focus on improving the segmentation quality, this is usually insufficient for instance segmentation of cell instances clumped together or with a small size. In this work, we…
Willard Zamora-Cárdenas, Mauro Mendez, L. Calefice, Martin Vargas + 5 more
'Gerardo Monge' 'Steve Quirós' 'David Elizondo' 'David Elizondo' 'Miguel A. Molina‐Cabello'] Abstract. Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic…
Kazuya Nishimura, Dai Fei Elmer Ker, Ryoma Bise
Cell shape analysis is important in biomedical research. Deep learning methods may perform to segment individual cells if they use sufficient training data that the boundary of each cell is annotated. However, it is very time-consuming for preparing such detailed annotation for many cell culture conditions. In this…
Anuradha Kar, Manuel Petit, Yassin Refahi, Guillaume Cerutt + 2 more
Segmenting three dimensional microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed which claim to provide high accuracy segmentation of cellular images and are increasingly…
Tomas Vicar, Jiri Chmelik, Roman Jakubicek, Larisa Chmelikova + 4 more
In this paper, U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is…
Chong Zhang, Shaofei Wang, Miguel Á. González Ballester, Julian Yarkony
'Julian Yarkony'] We study the problem of instance segmentation in biological images with crowded and compact cells. We formulate this task as an integer program where variables correspond to cells and constraints enforce that cells do not overlap. To solve this integer program, we propose a column generation…
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…
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…
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…
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…
Tim Van De Looverbosch, Sarah De Beuckeleer, Frederik De Smet, Jan Sijbers + 1 more
In the past decade, deep learning algorithms have surpassed the performance of many conventional image segmentation pipelines. Powerful models are now available for segmenting cells and nuclei in diverse 2D image types, but segmentation in 3D cell systems remains challenging due to the high cell density, the…
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…
Lin Zhang
Phase contrast microscopy (PCM) has been widely used in the biomedicine, which allows users to observe objectives without staining or killing them. One important related research is to employ PCM to monitor live cells. How to segment cell populations in obtained PCM images gains more and more attention as its a…
Lamees Nasser, Thomas Boudier
Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner.…
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…
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi + 6 more
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color)…
Authors not listed
During charge/discharge cracking occurs in Nickel Manganese Cobalt (NMC) secondary particles. Secondary particles with cracks will have observably lower grey-levels in micro-CT datasets compared to an otherwise identical pristine particle. This is due to the ‘partial volume effect’ where voxels representative of space…
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…