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Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

ABSTRACT: In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. The trained model showed 96% recall and 95% average Precision against the real-world test dataset. We show that our approach is effective also for various crops including rice, lettuce, oat, and wheat. Constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs for deploying deep learning-based analysis in the agricultural domain.

SUBMITTER: Toda Y

PROVIDER: S-EPMC7160130 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

Toda Yosuke Y Okura Fumio F Ito Jun J Okada Satoshi S Kinoshita Toshinori T Tsuji Hiroyuki H Saisho Daisuke D

Communications biology 20200415 1

In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of i ...[more]

PMID: 32296118

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Project description:MotivationSingle-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process.ResultsWe developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism.Availability and implementationThe synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:ImportanceDeep learning-based automatic surgical instrument recognition is an indispensable technology for surgical research and development. However, pixel-level recognition with high accuracy is required to make it suitable for surgical automation.ObjectiveTo develop a deep learning model that can simultaneously recognize 8 types of surgical instruments frequently used in laparoscopic colorectal operations and evaluate its recognition performance.Design, setting, and participantsThis quality improvement study was conducted at a single institution with a multi-institutional data set. Laparoscopic colorectal surgical videos recorded between April 1, 2009, and December 31, 2021, were included in the video data set. Deep learning-based instance segmentation, an image recognition approach that recognizes each object individually and pixel by pixel instead of roughly enclosing with a bounding box, was performed for 8 types of surgical instruments.Main outcomes and measuresAverage precision, calculated from the area under the precision-recall curve, was used as an evaluation metric. The average precision represents the number of instances of true-positive, false-positive, and false-negative results, and the mean average precision value for 8 types of surgical instruments was calculated. Five-fold cross-validation was used as the validation method. The annotation data set was split into 5 segments, of which 4 were used for training and the remainder for validation. The data set was split at the per-case level instead of the per-frame level; thus, the images extracted from an intraoperative video in the training set never appeared in the validation set. Validation was performed for all 5 validation sets, and the average mean average precision was calculated.ResultsIn total, 337 laparoscopic colorectal surgical videos were used. Pixel-by-pixel annotation was manually performed for 81 760 labels on 38 628 static images, constituting the annotation data set. The mean average precisions of the instance segmentation for surgical instruments were 90.9% for 3 instruments, 90.3% for 4 instruments, 91.6% for 6 instruments, and 91.8% for 8 instruments.Conclusions and relevanceA deep learning-based instance segmentation model that simultaneously recognizes 8 types of surgical instruments with high accuracy was successfully developed. The accuracy was maintained even when the number of types of surgical instruments increased. This model can be applied to surgical innovations, such as intraoperative navigation and surgical automation.

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Dataset Information

Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

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Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.

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