scholarly journals A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sumona Biswas ◽  
Shovan Barma
Keyword(s):  
Deep Learning ◽  
Plant Cell ◽  
Cell Biology ◽  
Large Scale ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Label Cell ◽  
Microscopy Image ◽  
Image Dataset ◽  
Safranin O

Abstract We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in deep learning (DL) framework. This dataset includes 9,811 unstained and 6,127 stained (safranin-o, toluidine blue-o, and lugol’s-iodine) images with three-fold annotation including physical, morphological, and tissue grading based on weight, different section area, and tissue zone respectively. In addition, we prepared ground truth segmentation labels for three different tuber weights. We have validated the pertinence of annotations by performing multi-label cell classification, employing convolutional neural network (CNN), VGG16, for unstained and stained images. The accuracy has been achieved up to 0.94, while, F2-score reaches to 0.92. Furthermore, the ground truth labels have been verified by semantic segmentation algorithm using UNet architecture which presents the mean intersection of union up to 0.70. Hence, the overall results show that the data are very much efficient and could enrich the domain of microscopy plant cell analysis for DL-framework.

Regularization strategies for deep-learning-based salt model building

2019 ◽  
Vol 7 (4) ◽  
pp. T911-T922
Author(s):  
Satyakee Sen ◽  
Sribharath Kainkaryam ◽  
Cen Ong ◽  
Arvind Sharma
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Model Building ◽  
Ground Truth ◽  
Real Data ◽  
Test Time ◽  
Generalization Error ◽  
Training Set ◽  
Production Scale ◽  
Ensemble Strategy

Salt model building has long been considered a severe bottleneck for large-scale 3D seismic imaging projects. It is one of the most time-consuming, labor-intensive, and difficult-to-automate processes in the entire depth imaging workflow requiring significant intervention by domain experts to manually interpret the salt bodies on noisy, low-frequency, and low-resolution seismic images at each iteration of the salt model building process. The difficulty and need for automating this task is well-recognized by the imaging community and has propelled the use of deep-learning-based convolutional neural network (CNN) architectures to carry out this task. However, significant challenges remain for reliable production-scale deployment of CNN-based methods for salt model building. This is mainly due to the poor generalization capabilities of these networks. When used on new surveys, never seen by the CNN models during the training stage, the interpretation accuracy of these models drops significantly. To remediate this key problem, we have introduced a U-shaped encoder-decoder type CNN architecture trained using a specialized regularization strategy aimed at reducing the generalization error of the network. Our regularization scheme perturbs the ground truth labels in the training set. Two different perturbations are discussed: one that randomly changes the labels of the training set, flipping salt labels to sediments and vice versa and the second that smooths the labels. We have determined that such perturbations act as a strong regularizer preventing the network from making highly confident predictions on the training set and thus reducing overfitting. An ensemble strategy is also used for test time augmentation that is shown to further improve the accuracy. The robustness of our CNN models, in terms of reduced generalization error and improved interpretation accuracy is demonstrated with real data examples from the Gulf of Mexico.

scholarly journals Development of a City-Scale Approach for Façade Color Measurement with Building Functional Classification Using Deep Learning and Street View Images

2021 ◽  
Vol 10 (8) ◽  
pp. 551
Author(s):  
Jiaxin Zhang ◽  
Tomohiro Fukuda ◽  
Nobuyoshi Yabuki
Keyword(s):  
Deep Learning ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Color Measurement ◽  
Functional Classification ◽  
Labor Costs ◽  
Color Distribution ◽  
Distribution Maps ◽  
Street View ◽  
Cost Efficient

Precise measuring of urban façade color is necessary for urban color planning. The existing manual methods of measuring building façade color are limited by time and labor costs and hardly carried out on a city scale. These methods also make it challenging to identify the role of the building function in controlling and guiding urban color planning. This paper explores a city-scale approach to façade color measurement with building functional classification using state-of-the-art deep learning techniques and street view images. Firstly, we used semantic segmentation to extract building façades and conducted the color calibration of the photos for pre-processing the collected street view images. Then, we proposed a color chart-based façade color measurement method and a multi-label deep learning-based building classification method. Next, the field survey data were used as the ground truth to verify the accuracy of the façade color measurement and building function classification. Finally, we applied our approach to generate façade color distribution maps with the building classification for three metropolises in China, and the results proved the transferability and effectiveness of the scheme. The proposed approach can provide city managers with an overall perception of urban façade color and building function across city-scale areas in a cost-efficient way, contributing to data-driven decision making for urban analytics and planning.

scholarly journals LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training

2020 ◽  
Vol 10 (14) ◽  
pp. 4913
Author(s):  
Tin Kramberger ◽  
Božidar Potočnik
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Dataset ◽  
Image Datasets

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.

scholarly journals EVICAN—a balanced dataset for algorithm development in cell and nucleus segmentation

2020 ◽  
Vol 36 (12) ◽  
pp. 3863-3870
Author(s):  
Mischa Schwendy ◽  
Ronald E Unger ◽  
Sapun H Parekh
Keyword(s):  
Deep Learning ◽  
Cell Biology ◽  
Ground Truth ◽  
Training Data ◽  
Training Dataset ◽  
Visual Cell ◽  
Application Development ◽  
Ground Truth Data ◽  
Quantitative Image ◽  
Nucleus Segmentation

Abstract Motivation Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. Results We present a new dataset, EVICAN—Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development. Availability and implementation The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=). Using a Mask R-CNN implementation, we demonstrate automated segmentation of cells and nuclei from brightfield images with a mean average precision of 61.6 % at a Jaccard Index above 0.5.

Taking the pulse of the Amazon rainforest by fusing multitemporal Sentinel 1 and 2 data for advanced deep-learning

2021 ◽  
Author(s):  
Melanie Brandmeier ◽  
Eya Cherif
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Supervised Learning ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Brazilian Amazon ◽  
Sensing Data ◽  
Weakly Supervised

<p>Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (e.g. Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable “ground truth” labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be used for weakly supervised training of deep-learning models that have a potential to improve predictions on higher resolution data nowadays available. The term weakly supervised learning was originally coined by (Zhou 2017) and refers to the attempt of constructing predictive models from incomplete, inexact and/or inaccurate labels as is often the case in remote sensing. To this end, we investigate advanced deep-learning strategies on Sentinel-1 timeseries and Sentinel-2 optical data to improve large-scale automatic mapping and monitoring of landcover changes in the Amazon area. Sentinel-1 data has the advantage to be resistant to cloud cover that often hinders optical remote sensing in the tropics.</p><p>We propose new architectures that are adapted to the particularities of remote sensing data (S1 timeseries and multispectral S2 data) and compare the performance to state-of-the-art models.  Results using only spectral data were very promising with overall test accuracies of 77.9% for Unet and 74.7% for a DeepLab implementation with ResNet50 backbone and F1 measures of 43.2% and 44.2% respectively.  On the other hand, preliminary results for new architectures leveraging the multi-temporal aspect of  SAR data have improved the quality of mapping, particularly for agricultural classes. For instance, our new designed network AtrousDeepForestM2 has a similar quantitative performances as DeepLab  (F1 of 58.1% vs 62.1%), however it produces better qualitative land cover maps.</p><p>To make our approach scalable and feasible for others, we integrate the trained models in a geoprocessing tool in ArcGIS that can also be deployed in a cloud environment and offers a variety of post-processing options to the user.</p><p>Souza, J., Carlos M., et al. (2013). "Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon." Remote Sensing 5(11): 5493-5513.   </p><p>Zhou, Z.-H. (2017). "A brief introduction to weakly supervised learning." National Science Review 5(1): 44-53.</p><p>"Project MapBiomas - Collection  4.1 of Brazilian Land Cover & Use Map Series, accessed on January 2020 through the link: https://mapbiomas.org/colecoes-mapbiomas?cama_set_language=en"</p>

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