7,815 research outputs found
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure
WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network
Deep learning has driven a great progress in natural and biological image
processing. However, in material science and engineering, there are often some
flaws and indistinctions in material microscopic images induced from complex
sample preparation, even due to the material itself, hindering the detection of
target objects. In this work, we propose WPU-net that redesigns the
architecture and weighted loss of U-Net, which forces the network to integrate
information from adjacent slices and pays more attention to the topology in
boundary detection task. Then, the WPU-net is applied into a typical material
example, i.e., the grain boundary detection of polycrystalline material.
Experiments demonstrate that the proposed method achieves promising performance
and outperforms state-of-the-art methods. Besides, we propose a new method for
object tracking between adjacent slices, which can effectively reconstruct 3D
structure of the whole material. Finally, we present a material microscopic
image dataset with the goal of advancing the state-of-the-art in image
processing for material science.Comment: technical repor
Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC
Background: High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed. Results: We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htbl concentrations decrease with replicative age. Conclusions: Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis.Peer reviewe
Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis
Neural networks promise to bring robust, quantitative analysis to medical
fields, but adoption is limited by the technicalities of training these
networks. To address this translation gap between medical researchers and
neural networks in the field of pathology, we have created an intuitive
interface which utilizes the commonly used whole slide image (WSI) viewer,
Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and
display of neural network predictions on WSIs. Leveraging this, we propose the
use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We
track network performance improvements as a function of iteration and quantify
the use of this pipeline for the segmentation of renal histologic findings on
WSIs. More specifically, we present network performance when applied to
segmentation of renal micro compartments, and demonstrate multi-class
segmentation in human and mouse renal tissue slides. Finally, to show the
adaptability of this technique to other medical imaging fields, we demonstrate
its ability to iteratively segment human prostate glands from radiology imaging
data.Comment: 15 pages, 7 figures, 2 supplemental figures (on the last page
Annotating Object Instances with a Polygon-RNN
We propose an approach for semi-automatic annotation of object instances.
While most current methods treat object segmentation as a pixel-labeling
problem, we here cast it as a polygon prediction task, mimicking how most
current datasets have been annotated. In particular, our approach takes as
input an image crop and sequentially produces vertices of the polygon outlining
the object. This allows a human annotator to interfere at any time and correct
a vertex if needed, producing as accurate segmentation as desired by the
annotator. We show that our approach speeds up the annotation process by a
factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement
in IoU with original ground-truth, matching the typical agreement between human
annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We
further show generalization capabilities of our approach to unseen datasets
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