Microscopy cell counting and detection with fully convolutional regression networks

By Weidi Xie, J. Alison Noble, Andrew Zisserman. Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2018

500 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f83c5daa07093e4c27af17f8f39a66e6196dcb74

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Machine learning applications in cell image analysis (2017, background): “44,45 IMPLICIT DATA REPRESENTATION Consider a different example, where ML is applied to whole images.”
Deep-STORM: super-resolution single-molecule microscopy by deep learning (2018, methodology): “The net-architecture is based on a fully convolutional encoder-decoder network and was inspired by previous work on cell counting [36].”
Counting Vehicles with Deep Learning in Onboard UAV Imagery (2019, methodology): “The problem of counting cells in microscopy images is tackled for example by [8], where a convolutional neural network is used to regress a cell spatial density map across the image.”
Improving Object Counting with Heatmap Regulation (2018, background): “Extensions to this basic idea are provided in [11,12,13,14,15].”
Deep neural network models for computational histopathology: A survey (2019, methodology): “In the context of FCN, the earlier methods by Chen et al. (2016a); Xie et al. (2018a) proposed a simple FCN based regression model for detecting cells in histopathology images.”
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond (2021, background): “Counting for specialized objects has a number of practical applications (Marsden et al., 2018), including but are not limited to cell counting (Xie et al., 2018), crowd counting (Sindagi & Patel, 2017), vehicle counting (Onoro-Rubio & L´opez-Sastre, 2016), wild-life counting (Arteta et al., 2016),…”
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy (2019, methodology): “More recently, methods based on deep learning have been shown to vastly improve results for nat-ural and biomedical images alike [11, 24, 25].”
Cell Detection with Star-convex Polygons (2018, background): “While this approach often gives good results, it is problematic for images of very crowded cell nuclei, since only a few mis-classified pixels can cause bordering but distinct cell instances to be fused [3,19].”
Deep Learning for Medical Image Processing: Overview, Challenges and Future (2017, methodology): “Fully CNN Deep learning has been used in [36] for auto-matic cell counting.”