Biology subjects

French, A. P.

Publications and source records attributed to French, A. P..

3 recordsLinked to original sources

bioRxiv2019

Volumetric Segmentation of Cell Cycle Markers in Confocal Images

I.AO_SCPCAPBSTRACTC_SCPCAPUnderstanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development - a process referred to as plant phenotyping - is increasingly important in the plant sciences, and is often a bottleneck. Automated tools are required to analyse the data in images depicting plant growth. In this paper, a deep learning approach is developed to locate fluorescent markers in 3D timeseries microscopy images. The approach is not dependant on marker morphology; only simple 3D point location annotations are required for training. The approach is evaluated on an unseen timeseries comprising several volumes, capturing growth of plants. Results are encouraging, with an average recall of 0.97 and average F-score of 0.78, despite only a very limited number of simple training annotations. In addition, an in-depth analysis of appropriate loss functions is conducted. To accompany [the finally-published] paper we are releasing the 4D point annotation tool used to generate the annotations, in the form of a plugin for the popular ImageJ (Fiji) software. Network models will be released online.

plant biology↗

bioRxiv2019

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools.

bioinformatics↗

bioRxiv2017

Deep Learning for Multi-task Plant Phenotyping

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation. We then present a deep learning approach capable of accurately localising wheat spikes and spikelets, despite the varied nature of this dataset. As well as locating features, our network offers near perfect counting accuracy for spikes (95.91%) and spikelets (99.66%). We also extend the network to perform simultaneous classification of images, demonstrating the power of multi-task deep architectures for plant phenotyping. We hope that our dataset will be useful to researchers in continued improvement of plant and crop phenotyping. With this in mind, alongside the dataset we will make all code and trained models available online.

plant biology↗