A Convolutional Neural Network for Spot Detection in Microscopy Images

2019 ◽  
pp. 132-145
Author(s):  
Matsilele Mabaso ◽  
Daniel Withey ◽  
Bhekisipho Twala
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Spot Detection ◽  
Microscopy Images

scholarly journals Biopolymer segmentation from CLSM microscopy images using a convolutional neural network

PAMM ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Pouyan Asgharzadeh ◽  
Annette I. Birkhold ◽  
Bugra Özdemir ◽  
Ralf Reski ◽  
Oliver Röhrle
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Microscopy Images

scholarly journals Detection of live breast cancer cells in brightfield microscopy images containing white blood cells by image analysis and deep learning

2021 ◽  
Author(s):  
Golnaz Moallem ◽  
Adity A. Pore ◽  
Anirudh Gangadhar ◽  
Hamed Sari-Sarraf ◽  
Siva A Vanapalli
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Cells ◽  
Blood Cells ◽  
Breast Cancer Cells ◽  
White Blood Cells ◽  
Size Difference ◽  
Microscopy Images

Significance: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. Aim: The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright field microscopy images that contain white blood cells (WBCs). Approach: We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate cancer cells from WBCs. The second approach is based on Faster Region-based Convolutional Neural Network (Faster R-CNN). Results: Both approaches detected cancer cells with high sensitivity and specificity with the Faster R-CNN being more efficient and suitable for deployment. The distinctive feature used by the CNN used to discriminate is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. Conclusions: CNN-based deep learning approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.

scholarly journals A convolutional neural network segments yeast microscopy images with high accuracy

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicola Dietler ◽  
Matthias Minder ◽  
Vojislav Gligorovski ◽  
Augoustina Maria Economou ◽  
Denis Alain Henri Lucien Joly ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Web Application ◽  
Large Scale ◽  
Model Organism ◽  
Fluorescent Markers ◽  
A Cell ◽  
Stressed Cells ◽  
Time Courses ◽  
Microscopy Images

AbstractThe identification of cell borders (‘segmentation’) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application (www.quantsysbio.com/data-and-software) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of ≈2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually.

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