SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research

Yaniv, Ziv; Lowekamp, Bradley; Johnson, Hans J.; Beare, Richard

doi:10.1007/s10278-017-0037-8

Cited by 305 publications

(194 citation statements)

References 26 publications

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“…In addition, researchers can link the executed notebook directly to papers (similarly to Table 2) and thus create an interactive publication with reproducible analysis. In the medical image analysis community, other examples of combined use of python and Jupyter notebooks are mainly for educational and general research purpose (e.g SimpleITK notebooks [77]), while usage of python as a programming language is rapidly gaining popularity in neuroimaging (e.g. Nipype [20]).…”

Section: Discussionmentioning

confidence: 99%

“…Finally, new notebooks could be added to the workflow to segment and analyze more knee tissues, such as tibial cartilage, patellar cartilage, and the menisci. Extensions will require a limited amount of effort because of the popularity and ease of python, the free availability of a large number of programming packages, and the flexibility of Jupyter notebooks [77]. In addition, standardized file format and computational environment will facilitate comparison of findings and performances of new algorithms.…”

Section: Discussionmentioning

confidence: 99%

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

Bonaretti

Gold

Beaupré

2019

Preprint

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Transparent 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 present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is 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. pyKNEEr facilitates 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, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings. IntroductionOpen science and computational reproducibility are recent movements in the scientific community that aim to promote and encourage transparent research. They are supported by national and international funding agencies, such as the United States National Institutes of Health (NIH) [9] and the European Commission [10]. Open science refers to the free availability of data, software, and methods developed by researchers with the aim to share knowledge and tools [75]. Computational reproducibility is the ability of researchers to duplicate the results of a previous study, using the same data, software, and methods used by the original authors [5]. Openness and reproducibility are essential to researchers to assess the accuracy of scientific claims [55], build on the work of other scientists with confidence and efficiency (i.e. without "reinventing the wheel") [54], and collaborate to improve and expand robust scientific workflows to accelerate scientific discoveries [53,14,42]. Historically, research data, tools, and processes were rarely openly available because of limited storage and computational power [42]. Nowadays, there are several opportunities to conduct transparent research: data repositories (e.g. Zenodo and FigShare), code repositories (e.g. GitHub, Git...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Bonaretti

Gold

Beaupré

2019

Preprint

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“…SimpleITK was used to resample short‐axis images to a common resolution of 1.5625

m m^{2} / p i x e l

and crop/zero‐pad to a common size of 192 × 192 and 256 × 256 for LVSC and ACDC dataset, respectively. Image intensities were clipped at 99

^{th}

percentile and normalized to zero mean and unit standard deviation.…”

Section: Methodsmentioning

confidence: 99%

A distance map regularized CNN for cardiac cine MR image segmentation

2019

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Purpose Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi‐task learning (MTL)‐based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images. Methods We train a CNN network to perform the main task of semantic segmentation, along with the simultaneous, auxiliary task of pixel‐wise distance map regression. The network also predicts uncertainties associated with both tasks, such that their losses are weighted by the inverse of their corresponding uncertainties. As a result, during training, the task featuring a higher uncertainty is weighted less and vice versa. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. The trained network outputs per‐pixel segmentation when a new patient cine MR image is provided as an input. Results We show that the proposed regularization method improves both binary and multi‐class segmentation performance over the corresponding state‐of‐the‐art CNN architectures. The evaluation was conducted on two publicly available cardiac cine MRI datasets, yielding average Dice coefficients of 0.84 ± 0.03 and 0.91 ± 0.04. We also demonstrate improved generalization performance of the distance map regularized network on cross‐dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56 ± 0.28 to 0.80 ± 0.14. Conclusions We have presented a method for accurate segmentation of cardiac structures from cine MR images. Our experiments verify that the proposed method exceeds the segmentation performance of three existing state‐of‐the‐art methods. Furthermore, several cardiac indices that often serve as diagnostic biomarkers, specifically blood pool volume, myocardial mass, and ejection fraction, computed using our method are better correlated with the indices computed from the reference, ground truth segmentation. Hence, the proposed method has the potential to become a non‐invasive screening and diagnostic tool for the clinical assessment of various cardiac conditions, as well as a reliable aid for generating patient specific models of the cardiac anatomy for therapy planning, simulation, and guidance.

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“…This documentation covers installation, fundamental concepts specific to SimpleITK ’s image and transformation elements, common API conventions, frequently asked questions and short example programs. Additional resources include a Jupyter notebook repository illustrating complete image-analysis workflows in Python and R (Yaniv, Lowekamp, Johnson, and Beare 2018, ; https://github.com/InsightSoftwareConsortium/SimpleITK-Notebooks) and a discourse discussion forum for users to post questions (https://discourse.itk.org/). …”

Section: Itk Simpleitk and The Simpleitk R Packagementioning

confidence: 99%

Image Segmentation, Registration and Characterization in R with SimpleITK

Beare¹,

Lowekamp²,

Yaniv³

2018

J. Stat. Soft.

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152

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Many types of medical and scientific experiments acquire raw data in the form of images. Various forms of image processing and image analysis are used to transform the raw image data into quantitative measures that are the basis of subsequent statistical analysis. In this article we describe the SimpleITK R package. SimpleITK is a simplified interface to the insight segmentation and registration toolkit (ITK). ITK is an open source C++ toolkit that has been actively developed over the past 18 years and is widely used by the medical image analysis community. SimpleITK provides packages for many interpreter environments, including R. Currently, it includes several hundred classes for image analysis including a wide range of image input and output, filtering operations, and higher level components for segmentation and registration. Using SimpleITK, development of complex combinations of image and statistical analysis procedures is feasible. This article includes several examples of computational image analysis tasks implemented using SimpleITK, including spherical marker localization, multi-modal image registration, segmentation evaluation, and cell image analysis.

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SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research

Cited by 305 publications

References 26 publications

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

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

A distance map regularized CNN for cardiac cine MR image segmentation

Image Segmentation, Registration and Characterization in R with SimpleITK

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