Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

• URL: http://arxiv.org/abs/2011.10505v1
• Date: Fri, 20 Nov 2020 17:05:36 GMT
• Title: Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation
• Authors: Leonid Mill, David Wolff, Nele Gerrits, Patrick Philipp, Lasse Kling, Florian Vollnhals, Andrew Ignatenko, Christian Jaremenko, Yixing Huang, Olivier De Castro, Jean-Nicolas Audinot, Inge Nelissen, Tom Wirtz, Andreas Maier, Silke Christiansen
• Abstract summary: We show that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. We derive a segmentation accuracy that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticles ensembles.
• Score: 5.927116192179681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as e.g. size, shape and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, we present an elegant, flexible and versatile method to bypass this costly and tedious data acquisition process. We show that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, we derive a segmentation accuracy that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which we chose as examples. Our study paves the way towards the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as microscopies and spectroscopies, for a wide variety of studies and applications, including the detection of plastic micro- and nanoparticles.

Related papers

• A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM) [1.0923877073891446]
  Construction Zone is a Python package for rapidly generating complex nanoscale atomic structures. We develop an end-to-end workflow for creating large simulated databases for training neural networks. Using our results, we are able to achieve state-of-the-art segmentation performance on experimental HRTEM images of nanoparticles.
  arXiv  Detail & Related papers  (2023-09-12T10:44:15Z)
• Deep Learning of Crystalline Defects from TEM images: A Solution for the Problem of "Never Enough Training Data" [0.0]
  In-situ TEM experiments can provide important insights into how dislocations behave and move. The analysis of individual video frames can provide useful insights but is limited by the capabilities of automated identification. In this work, a parametric model for generating synthetic training data for segmentation of dislocations is developed.
  arXiv  Detail & Related papers  (2023-07-12T17:37:46Z)
• ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning [102.46382882098847]
  We first investigate the effects of synthetic data in synthetic-to-real novel view synthesis. We propose to introduce geometry-aware contrastive learning to learn multi-view consistent features with geometric constraints. Our method can render images with higher quality and better fine-grained details, outperforming existing generalizable novel view synthesis methods in terms of PSNR, SSIM, and LPIPS.
  arXiv  Detail & Related papers  (2023-03-20T12:06:14Z)
• ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning [60.02503434201552]
  This research proposes learning approximations of complex exposures from training sets of simple ones. We demonstrate this approach to synthetic sensor responses surprisingly improves the detection of out-of-distribution obscured chemical analytes.
  arXiv  Detail & Related papers  (2023-02-09T20:19:57Z)
• Combining Variational Autoencoders and Physical Bias for Improved Microscopy Data Analysis [0.0]
  We present a physics augmented machine learning method which disentangles factors of variability within the data. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data.
  arXiv  Detail & Related papers  (2023-02-08T17:35:38Z)
• SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data [2.1616289178832666]
  We introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data. We demonstrate that it is possible to perform robust and expert-level segmentation tasks on previously unseen real-world data, without the need for manual annotations.
  arXiv  Detail & Related papers  (2022-07-29T12:50:32Z)
• Information-Theoretic Odometry Learning [83.36195426897768]
  We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
  arXiv  Detail & Related papers  (2022-03-11T02:37:35Z)
• Scene Synthesis via Uncertainty-Driven Attribute Synchronization [52.31834816911887]
  This paper introduces a novel neural scene synthesis approach that can capture diverse feature patterns of 3D scenes. Our method combines the strength of both neural network-based and conventional scene synthesis approaches.
  arXiv  Detail & Related papers  (2021-08-30T19:45:07Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.