scholarly journals 3D-Cell-Annotator: an open-source active surface tool for single cell segmentation in 3D microscopy images

2019 ◽  
Author(s):  
Ervin A. Tasnadi ◽  
Timea Toth ◽  
Maria Kovacs ◽  
Akos Diosdi ◽  
Francesco Pampaloni ◽  
...  
Keyword(s):  
Prior Information ◽  
Single Cells ◽  
Active Surface ◽  
Cell Segmentation ◽  
Shape Descriptors ◽  
Learning Approaches ◽  
3D Microscopy ◽  
Video Card ◽  
Bioimage Analysis ◽  
Microscopy Images

AbstractSummarySegmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a fully- and semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids, embryos) show that the precision of the segmentation reaches the level of a human expert.Availability and implementation3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA [email protected] and [email protected]

scholarly journals 3D-Cell-Annotator: an open-source active surface tool for single-cell segmentation in 3D microscopy images

2020 ◽  
Vol 36 (9) ◽  
pp. 2948-2949
Author(s):  
Ervin A Tasnadi ◽  
Timea Toth ◽  
Maria Kovacs ◽  
Akos Diosdi ◽  
Francesco Pampaloni ◽  
...  
Keyword(s):  
Prior Information ◽  
Single Cells ◽  
Active Surface ◽  
Cell Segmentation ◽  
Supplementary Information ◽  
Shape Descriptors ◽  
Learning Approaches ◽  
3D Microscopy ◽  
Bioimage Analysis ◽  
Microscopy Images

Abstract Summary Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids and embryos) show that the precision of the segmentation reaches the level of a human expert. Availability and implementation 3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA drivers. Supplementary information Supplementary data are available at Bioinformatics online.

SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images

2021 ◽  
pp. 1-1
Author(s):  
Yue Guo ◽  
Oleh Krupa ◽  
Jason Stein ◽  
Guorong Wu ◽  
Ashok Krishnamurthy
Keyword(s):  
Cell Counting ◽  
3D Microscopy ◽  
2D And 3D ◽  
Microscopy Images

scholarly journals PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Grzegorz Bokota ◽  
Jacek Sroka ◽  
Subhadip Basu ◽  
Nirmal Das ◽  
Pawel Trzaskoma ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Cell Nucleus ◽  
Ad Hoc ◽  
Multiple Use ◽  
Entry Barrier ◽  
Multi Scale ◽  
3D Microscopy ◽  
Nuclear Structures ◽  
Bulk Processing ◽  
Microscopy Images

Abstract Background Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation. Results In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. Conclusions In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

scholarly journals Training a deep learning model for single-cell segmentation without manual annotation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nizam Ud Din ◽  
Ji Yu
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Cell Segmentation ◽  
Learning Techniques ◽  
Signal Characteristics ◽  
Human Operators ◽  
Deep Learning Model ◽  
Microscopy Images ◽  
Segmentation Models

AbstractAdvances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy images. However, previous methods used a supervised training paradigm in order to create an accurate segmentation model. This strategy requires a large amount of manually labeled cellular images, in which accurate segmentations at pixel level were produced by human operators. Generating training data is expensive and a major hindrance in the wider adoption of machine learning based methods for cell segmentation. Here we present an alternative strategy that trains CNNs without any human-labeled data. We show that our method is able to produce accurate segmentation models, and is applicable to both fluorescence and bright-field images, and requires little to no prior knowledge of the signal characteristics.

scholarly journals Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells

2002 ◽  
Vol 24 (2-3) ◽  
pp. 101-111 ◽  
Author(s):  
Carolina Wählby ◽  
Joakim Lindblad ◽  
Mikael Vondrus ◽  
Ewert Bengtsson ◽  
Lennart Björkesten
Keyword(s):  
Statistical Analysis ◽  
Single Cells ◽  
Quality Measurement ◽  
Feedback System ◽  
Cell Segmentation ◽  
New Combination ◽  
Processing Step ◽  
Segmentation Quality ◽  
Fully Automatic

Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.

Joint Segmentation and Pairing of Nuclei and Golgi in 3D Microscopy Images

2021 ◽  
Author(s):  
Hemaxi Narotamo ◽  
Marie Ouarne ◽  
Claudio Areias Franco ◽  
Margarida Silveira
Keyword(s):  
3D Microscopy ◽  
Microscopy Images
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