ITK: enabling reproducible research and open science

By Matthew McCormick, Xiaoxiao Liu, Luis Ibáñez, J. Jomier, Charles Marion. Front. Neuroinform., 2014

360 citations recorded by the source corpus.

Reproducibility verification is essential to the practice of the scientific method. Researchers report their findings, which are strengthened as other independent groups in the scientific community share similar outcomes. In the many scientific fields where software has become a fundamental tool for capturing and analyzing data, this requirement of reproducibility implies that reliable and comprehensive software platforms and tools should be made available to the scientific community. The tools will empower them and the public to verify, through practice, the reproducibility of observations that are reported in the scientific literature. Medical image analysis is one of the fields in which the use of computational resources, both software and hardware, are an essential platform for performing experimental work. In this arena, the introduction of the Insight Toolkit (ITK) in 1999 has transformed the field and facilitates its progress by accelerating the rate at which algorithmic implementations are developed, tested, disseminated and improved. By building on the efficiency and quality of open source methodologies, ITK has provided the medical image community with an effective platform on which to build a daily workflow that incorporates the true scientific practices of reproducibility verification. This article describes the multiple tools, methodologies, and practices that the ITK community has adopted, refined, and followed during the past decade, in order to become one of the research communities with the most modern reproducibility verification infrastructure. For example, 207 contributors have created over 2400 unit tests that provide over 84% code line test coverage. The Insight Journal, an open publication journal associated with the toolkit, has seen over 360,000 publication downloads. The median normalized closeness centrality, a measure of knowledge flow, resulting from the distributed peer code review system was high, 0.46.

Subfields: Artificial Intelligence, Biophysics, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/98550574a6d1f373b8f2268a6b4f41f589e1fb24

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Ali Hatamizadeh

Citations in the corpus

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification (2021, methodology): “The DI-COM volume is read as an ITK image [29] and the skull-stripped volume is rigidly registered to it, providing a transformation matrix that deﬁnes the spatial mapping between the 2 volumes.”
MONAI: An open-source framework for deep learning in healthcare (2022, methodology): “MONAI provides adapter tools to accommodate the use of third-party transforms from packages such as ITK [13], torchIO [17], Kornia [18], BatchGenerator [19], Rising [20], and cuCIM [21].”
SCT: Spinal Cord Toolbox, an open‐source software for processing spinal cord MRI data (2017, methodology): “The registration procedure outputs forward (warp_anat2template.nii.gz) and backward (warp_template2anat.nii.gz) transformations in ITK format, which can be concatenated to subsequent transformations if needed.”
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks (2020, methodology): “structure to learn the dense non-linear correspondence between input scans, while DIF-VM is a probabilistic dieomorphic variant of VM. For Demons, we use the ocial implementation in the ITK toolkit [17]. For SyN, we adopt the ocial implementation in the ANTs software package [3]. The parameters in D”