• Corpus ID: 3656770

Using Deep Learning for Segmentation and Counting within Microscopy Data

  title={Using Deep Learning for Segmentation and Counting within Microscopy Data},
  author={Carlos X. Hern{\'a}ndez and Mohammad M. Sultan and Vijay S. Pande},
Cell counting is a ubiquitous, yet tedious task that would greatly benefit from automation. From basic biological questions to clinical trials, cell counts provide key quantitative feedback that drive research. Unfortunately, cell counting is most commonly a manual task and can be time-intensive. The task is made even more difficult due to overlapping cells, existence of multiple focal planes, and poor imaging quality, among other factors. Here, we describe a convolutional neural network… 

Figures from this paper

FASTER R-CNN for cell counting in low contrast microscopic images

  • A. UkaAleks TareX. PolisiIna Panci
  • Computer Science
    2020 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)
  • 2020
A convolutional neural network approach is described and test, employing CNN with region proposal called Faster-RCNN for cell counting in low contrast microscopic images, achieving an average precision of 79%.

Pyramidal Deep Neural Networks for the Accurate Segmentation and Counting of Cells in Microscopy Data

A cascade of networks that take as inputs different versions of the original image and utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor for cell counting.

Automated analysis of microscopy images using deep convolutional neural networks

The deep convolutional neural networks (DCNN) are used to classify the annotated images of five types of white blood cells and demonstrate that the DCNN model performs close to the accuracy of 80% and provides an accurate and fast method for hematological laboratories.

A Novel Convolutional Regression Network for Cell Counting

This method combines the advantages from both traditional segmentation-based and density-based methods and overcomes the limitations such as cell clumping, overlapping, and it can also bypass the fine-tuning step which was necessary for previous density- based methods when applying to different datasets.

Blood cells image segmentation and counting using deep transfer learning

This paper introduces a loss function for the Circle Hough Transform algorithm to further improve its accuracy and shows good results and has the potential to significantly reduce the time and effort required for manual blood cell counting.

Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines

This work proposes use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count and shows that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods.

A Deep Feature Learning Scheme for Counting the Cells in Microscopy Data

This work proposes a novel cell counting scheme that uses the features provided by a deep convolutional autoencoder (DCAE) as the inputs of a shallow regressor network, instead of using the segmentation masks.

Microscopy cell nuclei segmentation with enhanced U-Net

The results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.

Automated Counting of Cancer Cells by Ensembling Deep Features

Three deep convolutional neural network models were developed to regress image features to their cell counts in an end-to-end way and showed better performance in terms of smaller errors and larger correlations than those based on a single type of imaging feature.

Segmentation of Microscopy Data for finding Nuclei in Divergent Images

The strength of activation functions in medical image segmentation task is examined by improving learning rates by the proposed Carving Technique, which can annotate precise masks on test data by the network.

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

Deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

Annotated high-throughput microscopy image sets for validation

This work describes canonical ways to measure an algorithm’s performance so that algorithms can be compared against each other fairly, and provides an optional framework to do so conveniently within CellProfiler.

Towards Perspective-Free Object Counting with Deep Learning

A novel convolutional neural network solution, named Counting CNN (CCNN), formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps, able to estimate object densities in different very crowded scenarios.

Synthetic Images of High-Throughput Microscopy for Validation of Image Analysis Methods

A modular framework for simulating fluorescence microscopy images of cell populations and the ability to create high-throughput measurements provides a powerful tool for validating image analysis methods in traditional microscopy as well as in high content screening.

Computational Framework for Simulating Fluorescence Microscope Images With Cell Populations

A simulation platform for generating synthetic images of fluorescence-stained cell populations with realistic properties that enable the validation of analysis methods for automated image cytometry and comparison of their performance is presented.

Feature Pyramid Networks for Object Detection

This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.

Learning to Refine Object Segments

This work proposes to augment feedforward nets for object segmentation with a novel top-down refinement approach that is capable of efficiently generating high-fidelity object masks and is 50 % faster than the original DeepMask network.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.

Mask R-CNN

This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.

Very Deep Convolutional Networks for Large-Scale Image Recognition

This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.