Prof. Derk Joester Profile

Prof. Derk Joester

at Northwestern Univ

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Proceedings Article | 4 October 2024 Presentation + Paper

Adapting neural networks for rapid segmentation of mineralized tissues in mouse jaws

Victoria Cooley, Ethan Suwandi, Stuart Stock, Derk Joester

Proceedings Volume 13152, 131520V (2024) https://doi.org/10.1117/12.3027733

KEYWORDS: Enamels, Minerals, Image segmentation, Tissues, Attenuation, Bone, Teeth

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Congenital defects in dental enamel are diverse in pathology and etiology, and designing treatment tools for the clinic requires fundamental research on the process of enamel formation. Rodent incisors are the model of choice, and microcomputed tomography (μCT) is often the first method of comparison between models. Quantitative comparison of μCT data requires segmentation of mineralized tissues in the jaw; previously, we demonstrated the ability of convolutional neural networks to quickly and accurately segment mineral gradients in mouse jaws in synchrotron μCT images. Here we greatly expand on that work and present a protocol for adapting base networks to new pathologies and data types. With collaborators, we have amassed a collection (~80 TB) of μCT images from laboratory machines and synchrotrons representing 18 genetic mouse lines. We demonstrate the ability of adapted networks to segment these new data without compromising accuracy. Specifically, our networks adapted well to data collected with different x-ray sources, voxel dimensions, and phenotypes. In fully segmented data, we demonstrate the ability to visualize stages during enamel formation and compare rates of change in mineral density during the process. Importantly, our work has revealed insights about how and when mineral deposition goes awry in defective enamel. We envision widespread use of these tools. Once base networks are deployed to a repository for artificial neural networks, researchers will be able to use the protocol we present here for using modest amounts of their data to adapt a network to their own analysis.

Proceedings Article | 9 September 2021 Presentation + Paper

Semantic segmentation of mouse jaws using convolutional neural networks

Victoria Cooley, Stuart Stock, William Guise, Adya Verma, Tomas Wald, Ophir Klein, Derk Joester

Proceedings Volume 11840, 1184009 (2021) https://doi.org/10.1117/12.2594332

KEYWORDS: Minerals, Image segmentation, Bone, Tissues, Teeth, Convolutional neural networks, Neural networks, Mouse models

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Defects in tooth enamel are associated with a multitude of health conditions. An ongoing push to improve our understanding of enamel formation is generating a large number of mutant mouse lines to map protein function and create an Enamel Atlas. Reproducible analysis of a large amount of micro-CT data to compare these mouse lines necessitates an automated, high-throughput method of segmenting enamel, bone, and dentin. Neither simple binary segmentation nor region growing algorithms are effective for enamel in continuously growing mouse incisors due to the gradient in mineral content. To overcome these limitations, we have trained and validated a 3D convolutional neural network (CNN) to semantically segment mouse jaws. The network adopted a UNet architecture and incorporated training data from synchrotron- and laboratory-based sources. We evaluated the performance of the 3D CNN for the wildtype by comparing segmented outputs to ground truth labels and to outputs from a similarly trained 2D network. Next we tested the adaptability of the network by segmenting mutant tissues displaying phenotypes ranging in severity. Finally, we will demonstrate the use of CNN-segmented datasets to calculate metrics for quantitative comparison of the 3D mineral distribution between wildtype and mutant genotypes. We will discuss segmentation of the incisor, which allows us to track changes in the mineral during each developmental stage of enamel production. Our results show that the CNN-based segmentation and quantification pipeline is a versatile tool that will empower enamel researchers, help delineate mechanisms of disease, and enable the development of new approaches of intervention.

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