Generation of metrics by semantic segmentation of high speed lightning footage using machine learning

2020 ◽  
Author(s):  
Jason R Smit ◽  
Hugh GP Huntt ◽  
Tyson Cross ◽  
Carina Schumann ◽  
Tom A Warner
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Semantic Segmentation

scholarly journals LLAMA: a robust and scalable machine learning pipeline for analysis of large scale 4D microscopy data: analysis of cell ruffles and filopodia

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
James G. Lefevre ◽  
Yvette W. H. Koh ◽  
Adam A. Wall ◽  
Nicholas D. Condon ◽  
Jennifer L. Stow ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
High Speed ◽  
High Performance ◽  
Large Scale ◽  
Immune Surveillance ◽  
Complex Surface ◽  
Semantic Segmentation ◽  
Systematic Analysis ◽  
4D Microscopy

Abstract Background With recent advances in microscopy, recordings of cell behaviour can result in terabyte-size datasets. The lattice light sheet microscope (LLSM) images cells at high speed and high 3D resolution, accumulating data at 100 frames/second over hours, presenting a major challenge for interrogating these datasets. The surfaces of vertebrate cells can rapidly deform to create projections that interact with the microenvironment. Such surface projections include spike-like filopodia and wave-like ruffles on the surface of macrophages as they engage in immune surveillance. LLSM imaging has provided new insights into the complex surface behaviours of immune cells, including revealing new types of ruffles. However, full use of these data requires systematic and quantitative analysis of thousands of projections over hundreds of time steps, and an effective system for analysis of individual structures at this scale requires efficient and robust methods with minimal user intervention. Results We present LLAMA, a platform to enable systematic analysis of terabyte-scale 4D microscopy datasets. We use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, generating detailed object level statistics. Our system is designed to run on high-performance computing to achieve high throughput, with outputs suitable for visualisation and statistical analysis. Advanced visualisation is a key element of LLAMA: we provide a specialised tool which supports interactive quality control, optimisation, and output visualisation processes to complement the processing pipeline. LLAMA is demonstrated in an analysis of macrophage surface projections, in which it is used to i) discriminate ruffles induced by lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1) and ii) determine the autonomy of ruffle morphologies. Conclusions LLAMA provides an effective open source tool for running a cell microscopy analysis pipeline based on semantic segmentation, object analysis and tracking. Detailed numerical and visual outputs enable effective statistical analysis, identifying distinct patterns of increased activity under the two interventions considered in our example analysis. Our system provides the capacity to screen large datasets for specific structural configurations. LLAMA identified distinct features of LPS and CSF-1 induced ruffles and it identified a continuity of behaviour between tent pole ruffling, wave-like ruffling and filopodia deployment.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Understanding Rooftop PV Panel Semantic Segmentation of Satellite and Aerial Images for Better Using Machine Learning

2021 ◽  
pp. 100057
Author(s):  
Peiran Li ◽  
Haoran Zhang ◽  
Zhiling Guo ◽  
Suxing Lyu ◽  
Jinyu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Pv Panel

scholarly journals Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1365
Author(s):  
Tao Zheng ◽  
Zhizhao Duan ◽  
Jin Wang ◽  
Guodong Lu ◽  
Shengjie Li ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed ◽  
High Performance ◽  
High Efficiency ◽  
Semantic Segmentation ◽  
Raspberry Pi ◽  
Distance Transform ◽  
Testing Stage ◽  
Sampling Points ◽  
Information Sampling

Semantic segmentation of room maps is an essential issue in mobile robots’ execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943.

Geometrical design of a crystal growth system guided by a machine learning algorithm

CrystEngComm ◽  
2021 ◽  
Author(s):  
Wancheng Yu ◽  
Can Zhu ◽  
Yosuke Tsunooka ◽  
Wei Huang ◽  
Yifan Dang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crystal Growth ◽  
High Speed ◽  
Learning Algorithm ◽  
Computational Techniques ◽  
Machine Learning Algorithm ◽  
Geometrical Design ◽  
Large Numbers ◽  
Growth System ◽  
Speed Method

This study proposes a new high-speed method for designing crystal growth systems. It is capable of optimizing large numbers of parameters simultaneously which is difficult for traditional experimental and computational techniques.

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