scholarly journals pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

2019 ◽  
Author(s):  
Serena Bonaretti ◽  
Garry E. Gold ◽  
Gary S. Beaupre
Keyword(s):  
Image Analysis ◽  
Cartilage Degeneration ◽  
Reproducible Research ◽  
Knee Cartilage ◽  
Mr Images ◽  
Image Preprocessing ◽  
Algorithm Comparison ◽  
Analysis Workflow ◽  
Code Extension ◽  
Programming Skills

AbstractTransparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We presentpyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images.pyKNEEris written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics, 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions, and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments.pyKNEErfacilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure,pyKNEErfavors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research withpyKNEEr. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.

scholarly journals pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0226501
Author(s):  
Serena Bonaretti ◽  
Garry E. Gold ◽  
Gary S. Beaupre
Keyword(s):  
Image Analysis ◽  
Reproducible Research ◽  
Knee Cartilage ◽  
Analysis Workflow

scholarly journals KCB-Net: A 3D Knee Cartilage and Bone Segmentation Network via Sparse Annotation

2021 ◽  
Author(s):  
Yaopeng Peng ◽  
Hao Zheng ◽  
Fahim Zaman ◽  
Lichun Zhang ◽  
Xiaodong Wu ◽  
...  
Keyword(s):  
Medical Image Segmentation ◽  
Small Subset ◽  
Bone Segmentation ◽  
Knee Cartilage ◽  
Mr Images ◽  
3D Images ◽  
Articular Damage ◽  
Primal Dual ◽  
Model Training ◽  
New Framework

<div>Knee cartilage and bone segmentation is critical for physicians to analyze and diagnose articular damage and knee osteoarthritis (OA). Deep learning (DL) methods for medical image segmentation have largely outperformed traditional methods, but they often need large amounts of annotated data for model training, which is very costly and time-consuming for medical experts, especially on 3D images. In this paper, we report a new knee cartilage and bone segmentation framework, KCB-Net, for 3D MR images based on sparse annotation. KCB-Net selects a small subset of slices from 3D images for annotation, and seeks to bridge the performance gap between sparse annotation and full annotation. Specifically, it first identifies a subset of the most effective and representative slices with an unsupervised scheme; it then trains an ensemble model using the annotated slices; next, it self-trains the model using 3D images containing pseudo-labels generated by the ensemble method and improved by a bi-directional hierarchical earth mover’s distance (bi-HEMD) algorithm; finally, it fine-tunes the segmentation results using the primal-dual Internal Point Method (IPM). Experiments on two 3D MR knee joint datasets (the Iowa dataset and iMorphics dataset) show that our new framework outperforms state-of-the-art methods on full annotation, and yields high quality results even for annotation ratios as low as 5%.<br></div>

Optimization of local shape and appearance probabilities for segmentation of knee cartilage in 3-D MR images

2011 ◽  
Vol 115 (12) ◽  
pp. 1710-1720 ◽  
Author(s):  
Soochahn Lee ◽  
Sang Hyun Park ◽  
Hackjoon Shim ◽  
Il Dong Yun ◽  
Sang Uk Lee
Keyword(s):  
Knee Cartilage ◽  
Mr Images ◽  
Local Shape

scholarly journals RASflow: An RNA-Seq Analysis Workflow with Snakemake

2019 ◽  
Author(s):  
Xiaokang Zhang ◽  
Inge Jonassen
Keyword(s):  
Gene Expression ◽  
Management System ◽  
Workflow Management ◽  
Model Organisms ◽  
Gene Transcript ◽  
Rna Seq ◽  
Public Data ◽  
Wide Range ◽  
Analysis Workflow ◽  
Programming Skills

AbstractBackgroundWith the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq data has to be processed through a number of steps resulting in a quantification of expression of each gene / transcript in each of the analyzed samples. A number of workflows are available to help researchers perform these steps on their own data, or on public data to take advantage of novel software or reference data in data re-analysis. However, many of the existing workflows are limited to specific types of studies. We therefore aimed to develop a maximally general workflow, applicable to a wide range of data and analysis approaches and at the same time support research on both model and non-model organisms. Furthermore, we aimed to make the workflow usable also for users with limited programming skills.ResultsUtilizing the workflow management system Snakemake and the package management system Conda, we have developed a modular, flexible and user-friendly RNA-Seq analysis pipeline: RNA-Seq Analysis Snakemake Workflow (RASflow). Utilizing Snakemake and Conda alleviates challenges with library dependencies and version conflicts and also supports reproducibility. To be applicable for a wide variety of applications, RASflow supports mapping of reads to both genomic and transcriptomic assemblies. RASflow has a broad range of potential users: it can be applied by researchers interested in any organism and since it requires no programming skills, it can be used by researchers with different backgrounds. RASflow is an open source tool and source code as well as documentation, tutorials and example data sets can be found on GitHub: https://github.com/zhxiaokang/RASflowConclusionsRASflow is a simple and reliable RNA-Seq analysis workflow which is a full pack of RNA-Seq analysis.

scholarly journals AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES

2016 ◽  
Vol XLI-B1 ◽  
pp. 1085-1091 ◽  
Author(s):  
Huai Yu ◽  
Tianheng Yan ◽  
Wen Yang ◽  
Hong Zheng
Keyword(s):  
Image Analysis ◽  
Spatial Information ◽  
Geometric Calibration ◽  
Integrative Framework ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Binary Partition ◽  
Analysis Workflow ◽  
Uav Images ◽  
Definition Of

In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.

scholarly journals Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research

2021 ◽  
Author(s):  
Zena Lapp ◽  
Kelly L Sovacool ◽  
Nicholas A Lesniak ◽  
Dana King ◽  
Catherine Barnier ◽  
...  
Keyword(s):  
Data Analysis ◽  
Learning Objectives ◽  
Version Control ◽  
Reproducible Research ◽  
Entry Barrier ◽  
Individual Level ◽  
New Curriculum ◽  
Programming Skills ◽  
R Programming ◽  
Reporting Data

Inspired by well-established material and pedagogy provided by The Carpentries, we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse, the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum (https://umcarpentries.org/intro-curriculum-r/) that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.

scholarly journals ZELDA: a 3D Image Segmentation and Parent-Child relation plugin for microscopy image analysis in napari

2021 ◽  
Author(s):  
Rocco D'Antuono ◽  
Giuseppina Pisignano
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Open Source ◽  
Cell Number ◽  
Cell Segmentation ◽  
Data Sets ◽  
3D Analysis ◽  
3D Segmentation ◽  
Analysis Workflow ◽  
Parent Child

Bioimage analysis workflows allow the measurement of sample properties such as fluorescence intensity and polarization, cell number, and vesicles distribution, but often require the integration of multiple software tools. Furthermore, it is increasingly appreciated that to overcome the limitations of the 2D-view-based image analysis approaches and to correctly understand and interpret biological processes, a 3D segmentation of microscopy data sets becomes imperative. Despite the availability of numerous algorithms for the 2D and 3D segmentation, the latter still offers some challenges for the end-users, who often do not have either an extensive knowledge of the existing software or coding skills to link the output of multiple tools. While several commercial packages are available on the market, fewer are the open-source solutions able to execute a complete 3D analysis workflow. Here we present ZELDA, a new napari plugin that easily integrates the cutting-edge solutions offered by python ecosystem, such as scikit-image for image segmentation, matplotlib for data visualization, and napari multi-dimensional image viewer for 3D rendering. This plugin aims to provide interactive and zero-scripting customizable workflows for cell segmentation, vesicles counting, parent-child relation between objects, signal quantification, and results presentation; all included in the same open-source napari viewer, and 'few clicks away'.

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