Characterization of bone abnormalities from micro-CT images for evaluating drug toxicity in developmental and reproductive toxicology (DART) studies

Dogdas, Belma; Chen, Antong; Mehta, Saurin; Shah, Tosha; Robinson, Barbara L.; Xue, Dan; Gleason, Alexa; Wise, L. David; Crawford, Randy; Pak, Irene; Cruz, Francisco; Somayajula, Sangeetha; Bagchi, Ansu; Johnson, Cynthia D.; Mattson, Britta A.; Winkelmann, Chris

doi:10.1109/isbi.2015.7163962

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…In fact, Wise, Winkelmann, Dogdas, and Bagchi () published work describing methodology to automate skeletal examinations. In addition, Dogdas et al () using a test data set of 167 fetuses, with verified skeletal abnormalities, demonstrated a sensitivity of 0.959 and a specificity of 0.805 for the software program. Additional advantages of using micro‐CT methodology compared with Alizarin Red S staining include, increasing the quality of data by making it easier and convenient to peer review observations, allowing digital sharing with regulatory agencies and simplifying archival of study information, reducing time and labor spent on these studies, collecting quantitative data by measuring bone lengths, volumes, and mineral density, and utilizing procedures that support environmental sustainability.…”

Section: Discussionmentioning

confidence: 99%

The use of micro‐CT imaging to examine and illustrate fetal skeletal abnormalities in Dutch Belted rabbits and to prove concordance with Alizarin Red stained skeletal examination

Solomon

Murzyn

Rendemonti

et al. 2017

Birth Defects Research

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Section: Discussionmentioning

confidence: 99%

The use of micro‐CT imaging to examine and illustrate fetal skeletal abnormalities in Dutch Belted rabbits and to prove concordance with Alizarin Red stained skeletal examination

Solomon

Murzyn

Rendemonti

et al. 2017

Birth Defects Research

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“…We adopted a nonlinear registration algorithm which automatically computes the landmark positions from the ones that are pre-defined. The registration algorithm based on diffeomorphic mapping has been successfully used in brain analyses [21] but is rarely used in bone analysis [22,23].…”

Section: Introductionmentioning

confidence: 99%

Shape morphing technique can accurately predict pelvic bone landmarks

et al. 2021

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Diffeomorphic shape registration allows for the seamless geometric alignment of shapes. In this study, we demonstrated the use of a registration algorithm to automatically seed anthropological landmarks on the CT images of the pelvis. We found a high correlation between manually and automatically seeded landmarks. The registration algorithm makes it possible to achieve a high degree of automation with the potential to reduce operator errors in the seeding of anthropological landmarks. The results of this study represent a promising step forward in effectively defining the anthropological measures of the human skeleton.

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“…Manufacturing (bio)pharmaceuticals requires strict Developmental And Reproductive Toxicology (DART) studies on animal fetuses to investigate drug-induced birth defects [1,2,3]. Although manual inspection with Alizarin Red staining is current practice for detecting skeletal abnormalities, it is a long, laborious, and non-quantitative procedure [4]. In this regard, Machine Learning can enable fast and quantitative automated screening; it has shown great promise in human skeletal localization [5], labeling [6], and abnormality detection [7] on CT scans.…”

Section: Introductionmentioning

confidence: 99%

Effort-Free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-Body Micro-Ct Scans

Fukuda¹,

Han²,

Hakamada³

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Machine Learning-based fast and quantitative automated screening plays a key role in analyzing human bones on Computed Tomography (CT) scans. However, despite the requirement in drug safety assessment, such research is rare on animal fetus micro-CT scans due to its laborious data collection and annotation. Therefore, we propose various bone feature engineering techniques to thoroughly automate the skeletal localization/labeling/abnormality detection of rat fetuses on whole-body micro-CT scans with minimum effort. Despite limited training data of 49 fetuses, in skeletal labeling and abnormality detection, we achieve accuracy of 0.900 and 0.810, respectively.

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Characterization of bone abnormalities from micro-CT images for evaluating drug toxicity in developmental and reproductive toxicology (DART) studies

Cited by 7 publications

References 5 publications

The use of micro‐CT imaging to examine and illustrate fetal skeletal abnormalities in Dutch Belted rabbits and to prove concordance with Alizarin Red stained skeletal examination

The use of micro‐CT imaging to examine and illustrate fetal skeletal abnormalities in Dutch Belted rabbits and to prove concordance with Alizarin Red stained skeletal examination

Shape morphing technique can accurately predict pelvic bone landmarks

Effort-Free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-Body Micro-Ct Scans

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