Abstract
BackgroundA common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identifying individual cells as well as their phenotype in one step. State-of-the-art deep learning for object detection is poised to improve the accuracy and efficiency of biological image analysis.ResultsWe created Keras R-CNN to bring leading computational research to the everyday practice of bioimage analysts. Keras R-CNN implements deep learning object detection techniques using Keras and Tensorflow (https://github.com/broadinstitute/keras-rcnn). We demonstrate the command line tool’s simplified Application Programming Interface on two important biological problems, nucleus detection and malaria stage classification, and show its potential for identifying and classifying a large number of cells. For malaria stage classification, we compare results with expert human annotators and find comparable performance.ConclusionsKeras R-CNN is a Python package that performs automated cell identification for both brightfield and fluorescence images and can process large image sets. Both the package and image datasets are freely available on GitHub and the Broad Bioimage Benchmark Collection.
Highlights
A common yet still manual task in basic biology research, highthroughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images
Deep learning-based object detection algorithms (Fig. 1b) examine the raw pixels of images and discover which features and combinations of features best describe each phenotypic class of cells, with minimal user configuration of mathematical parameters
We recently described instance segmentation of nuclei [16]; here we instead address the problem of object detection, which yields bounding boxes and whose accuracy is assessed by a different metric
Summary
A common yet still manual task in basic biology research, highthroughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Traditional segmentation methods (Fig. 1a) identify individual pixels that belong to each distinct object through a carefully designed series of image processing steps, often involving watershed, distance transform, and intensity gradients This approach requires algorithm selection and parameter tuning (and time and expertise), is computationally expensive, and. Deep learning-based object detection algorithms (Fig. 1b) examine the raw pixels of images and discover which features and combinations of features best describe each phenotypic class of cells (based on examples provided by the researcher), with minimal user configuration of mathematical parameters. They can conveniently identify cells as well as their phenotype in one step. For cases where pixel-level segmentation is needed, object detection can be followed by post-processing steps to define precise boundaries for each object
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