Open science – combining open data and open source software: Medical image analysis with the Insight Toolkit

Yoo, Terry S.; Metaxas, Dimitris N.

doi:10.1016/j.media.2005.04.008

Cited by 37 publications

(31 citation statements)

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“…We have implemented texture features extraction using a MIM Java plugin that interfaces to an in-house software developed in C ++ using Insight Segmentation and Registration Toolkit (ITK) (36,37). The ITK is an open-source, cross-platform system developed in C ++ that provides tools for the development of image analysis.…”

Section: Radiomics Pipelinementioning

confidence: 99%

Prostate cancer radiomics and the promise of radiogenomics

Stoyanova

Takhar

Tschudi

et al. 2016

Transl. Cancer Res.

109

104

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Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinicalpathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing Author Manuscript aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. HHS Public Access

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Section: Radiomics Pipelinementioning

confidence: 99%

Prostate cancer radiomics and the promise of radiogenomics

Stoyanova

Takhar

Tschudi

et al. 2016

Transl. Cancer Res.

109

104

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“…The proposed approach has been applied to 18 sets of T1W-MRI volumetric images. The results are compared with the region growing algorithm implemented in ITK [18] . Fig.7 shows the tracing and segmentation results of slice 174, 216, and 248 by our approach.…”

Section: Resultsmentioning

confidence: 99%

Automatic tracing and segmentation of rat mammary fat pads in MRI image sequences based on cartoon-texture model

Zhang

Chen

et al. 2009

Trans. Tianjin Univ.

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Abstract：The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer. Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues. Rat fat pads may lose continuity along image sequences or adjoin similar intensity areas like epidermis and subcutaneous regions. A new approach for automatic tracing and segmentation of fat pads in magnetic resonance imaging (MRI) image sequences is presented, which does not require that the number of pads be constant or the spatial location of pads be adjacent among image slices. First, each image is decomposed into cartoon image and texture image based on cartoon-texture model. They will be used as smooth image and feature image for segmentation and for targeting pad seeds, respectively. Then, two-phase direct energy segmentation based on Chan-Vese active contour model is applied to partitioning the cartoon image into a set of regions, from which the pad boundary is traced iteratively from the pad seed. A tracing algorithm based on scanning order is proposed to accurately trace the pad boundary, which effectively removes the epidermis attached to the pad without any post processing as well as solves the problem of over-segmentation of some small holes inside the pad. The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.Breast cancer is by far one of the most frequently diagnosed cancers in women. Among all the dietary factors, alcohol is most consistently associated with an increase of breast cancer risk. Quantitative measurements of features regarding glands, e.g. the growth patterns of mammary fat pad and the glandular tissues inside the fat pad, are indispensable to studying the relationship between alcohol and breast cancer. In one study of diet, alcohol and mammary cancer, we measured the size of fat pads over the time and quantified the development of glandular structures in T1 weighted (T1W) magnetic resonance imaging (MRI) rat images [1] . Since these measurements and quantification are available after delineation of different tissues, accurate segmentation of mammary fat pad and extraction of glandular tissue are key steps in calculating the measurement.In normal conditions, the rat mammary apparatus is formed by 6 pairs of glands. Three are situated in the thorax and 3 in the abdominal-inguinal region. In T1W-MRI images, the mammary glands appear as bright areas below the epidermis. Glandular tissues appear as grayer blobs inside the glands, of varying contrast to their surrounding fatty areas. In this paper, we only focus on automatic segmentation of mammary fat pads in volumetric sets of MRI images.The Chan-Vese active contour models [2] , similar to the segmentation method developed by Tsai et al [3] , have been widely used in image segmentation. These active contour models can be put into a level-set framework [4][5][6] introduced by ...

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“…This problem of duplication and insufficient generalisability can be addressed if schemes for sharing encompass not only the data but also the methods and tools applied to the data. One success story in this regard is the Insight Toolkit (Yoo & Metaxas, 2005), an applications programming interface for high-dimensional image processing which arose from a collaborative workshop on the Visible Human Project.…”

Section: Sharing Of Analytical Methods Tools and Processingmentioning

confidence: 99%

Offering to Share: How to Put Heads Together in Autism Neuroimaging

Belmonte

Mazziotta

Minshew

et al. 2007

J Autism Dev Disord

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Data sharing in autism neuroimaging presents scientific, technical, and social obstacles. We outline the desiderata for a data-sharing scheme that combines imaging with other measures of phenotype and with genetics, defines requirements for comparability of derived data and NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript recommendations for raw data, outlines a core protocol including multispectral structural and diffusion-tensor imaging and optional extensions, provides for the collection of prospective, confound-free normative data, and extends sharing and collaborative development not only to data but to the analytical tools and methods applied to these data. A theme in these requirements is the need to preserve creative approaches and risk-taking within individual laboratories at the same time as common standards are provided for these laboratories to build on. KeywordsImaging; MRI; PET; Morphometry; Segmentation; Data sharing The ProblemTracing the behaviourally defined syndrome of autism to its neurobiological roots poses a difficulty since autism is heterogeneous in terms of its detailed symptom profiles (Ronald, Happé, & Plomin, 2005), genetic and environmental antecedents (Veenstra-Vanderweele, Christian, & Cook, 2004) and developmental mechanisms (Belmonte et al., 2004a). Because autism in this regard may be an amalgam of many unknown conditions, it seems a foregone conclusion that behaviourally ascertained groups of subjects contain large amounts of unmodelled variance, and that the relation between group size and statistical power is a steep one (Coon, 2006). This problem of sample size in the context of heterogeneous conditions is particularly acute in the domain of brain imaging, where costs are great and small samples are therefore more accepted and more usual. A solution seems clear in principle: the many small data sets collected by various investigators ought to be pooled into one large data set for analysis. Several obstacles, though, make such data sharing easier said than done. These obstacles are scientific, technical, and social-but not insurmountable.The scientific obstacles are matters of sample heterogeneity, which complicate the comparability of separately ascertained groups. This heterogeneity is both longitudinal and cross-sectional. Longitudinally, especially given autism's nature as a developmental disorder, measurements can be expected to change over the course of maturation and aging (Aylward, Minshew, Field, Sparks, & Singh, 2002;Carper, Moses, Tigue, & Courchesne, 2002). [The inconsistency of recent findings on the size of the amygdala at various ages is a case in point (Baron-Cohen, Knickmeyer, & Belmonte, 2005).] The consequent need to control and account for age particularly hampers retrospective efforts to combine separately ascertained samples. Cross-sectionally, autism's multiplicity of symptom profiles and neurobiological mechanisms makes it imperative to correlate imaging with other measures of potential endophenotypes, heightening the...

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Open science – combining open data and open source software: Medical image analysis with the Insight Toolkit

Cited by 37 publications

References 0 publications

Prostate cancer radiomics and the promise of radiogenomics

Prostate cancer radiomics and the promise of radiogenomics

Automatic tracing and segmentation of rat mammary fat pads in MRI image sequences based on cartoon-texture model

Offering to Share: How to Put Heads Together in Autism Neuroimaging

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