Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs

by   fds.fds, et al.

In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an in-depth analysis of key model performance statistics, with a focus on quantities such as predicted distributions of defect shapes, defect sizes, and defect areal densities relevant to informing modeling and understanding of irradiated Fe-based materials properties. To better understand the performance and present limitations of the model, we provide examples of useful evaluation tests which include a suite of random splits, and dataset size-dependent and domain-targeted cross validation tests. Overall, we find that the current model is a fast, effective tool for automatically characterizing and quantifying multiple defect types in microscopy images, with a level of accuracy on par with human domain expert labelers. More specifically, the model can achieve average defect identification F1 scores as high as 0.8, and, based on random cross validation, have low overall average (+/- standard deviation) defect size and density percentage errors of 7.3 (+/- 3.8) respectively. Further, our model predicts the expected material hardening to within 10-20 MPa (about 10 level as experiments. Our targeted evaluation tests also suggest the best path toward improving future models is not expanding existing databases with more labeled images but instead data additions that target weak points of the model domain, such as images from different microscopes, imaging conditions, irradiation environments, and alloy types. Finally, we discuss the first phase of an effort to provide an easy-to-use, open-source object detection tool to the broader community for identifying defects in new images.



There are no comments yet.


page 6

page 12

page 14

page 15


m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks

Autonomous surgical procedures, in particular minimal invasive surgeries...

Adapting Mask-RCNN for Automatic Nucleus Segmentation

Automatic segmentation of microscopy images is an important task in medi...

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Segmentation of axon and myelin from microscopy images of the nervous sy...

DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction

Maps of brain microarchitecture are important for understanding neurolog...

Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI

Semantic segmentation using convolutional neural networks (CNNs) is the ...

Learning-based Defect Recognition for Quasi-Periodic Microscope Images

The detailed control of crystalline material defects is a crucial proces...

Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow

Fourier ptychography is a recently developed imaging approach for large ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.