Dr. Victoria Cooley Profile

Dr. Victoria Cooley

at Argonne National Lab

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Publications (4)

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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 | 14 October 2022 Presentation + Paper

Growth bands in dogfish spines

Stuart Stock, Victoria Cooley, Cindy Tribuzio, Luxi Li, Viktor Nikitin, Olga Antipova

Proceedings Volume 12242, 122420S (2022) https://doi.org/10.1117/12.2633826

KEYWORDS: Spine, Tissues, X-ray fluorescence spectroscopy, X-rays, Minerals, Signal attenuation, Zinc, Synchrotrons, Bone, Phase contrast

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Dogfish (Squalus spp.) possess one or two dorsal-fin spines located at the dorsal midline over the vertebral column. These spines are heavily mineralized, and surface bands of light and dark contrast are used in age determinations. The interior of the spine also contains growth bands visible via optical microscopy of thin sections, but the three-dimensional pattern of growth bands does not appear to have been quantitatively mapped nor has the microstructural origin of the band contrast been established. This paper reports synchrotron microComputed Tomography (microCT) results on spines of Squalus suckleyi. MicroCT was performed at beamline 2-BM of the Advanced Photon Source (APS). There are numerous parallel bands, and their contrast consists of varying (higher and lower) values of linear attenuation coefficient, similar to growth bands observed in other mineralized tissues including mammalian cementum and dentin. The microCT data are supplemented by x-ray excited x-ray fluorescence maps of a sectioned Pacific dogfish spine recorded at beamlines 8-BM and 2-ID-E, APS; of particular note are bands of high Zn content, something which has been found in growth bands in other mineralized tissues.

Proceedings Article | 14 October 2022 Presentation + Paper

Metal pin artifact reduction with masked iterative nano-CT reconstruction

Mengzhou Li, Victoria Cooley, Viktor Nikitin, Stuart Stock, Ge Wang

Proceedings Volume 12242, 122421H (2022) https://doi.org/10.1117/12.2635261

KEYWORDS: Metals, Image segmentation, Teeth, Tomography, Lawrencium, Image quality, X-rays, Synchrotron radiation, Signal attenuation, X-ray microscopy, X-ray computed tomography

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Nano-CT enables 3D imaging of micro/nano-structures and is becoming an indispensable tool. At such a high resolution, specimens must have a small diameter small enough to fit into the instrument’s field of view, typically a few tens of micrometers to a few hundred micrometers. As a result, samples are commonly glued to the tip of a steel pin for alignment before imaging. Ideally, data are collected from the part of a specimen above the pin and the x-ray opaque pin will not interfere with imaging. However, the tiny sample size makes precise mounting very tricky, and many times a region adjacent to the pin is found of to be of interest post mounting. Sometimes the sample is too fragile to remount and other times removing the specimen and repeating the tedious remounting steps is impractical due to time constraints, we find that the information occluded by the metal pin can be almost fully recovered via iterative reconstruction with simple metal-trace masked from a regular scan of an imperfectly mounted specimen. Specifically, combining the metal artifact reduction and interior tomography techniques, a metal trace mask in the sinogram is first extracted from a low-resolution global reconstruction which covers the whole cross-section of the pin, then the desired high-resolution reconstruction of a region of interest is iteratively reconstructed excluding any contribution from the metal trace. Our method is demonstrated with a 42.35 nm reconstruction of a portion of a sea urchin tooth, which is scanned on a synchrotron with the pin moving across the field of view during sample rotation, showing that streaking artifacts caused by pin occlusion can be greatly suppressed to achieve an image quality close to that without occlusion. These results suggest that our method has a great potential in simplifying the specimen preparation and relaxing the proficiency requirements, which significantly facilitates nano-CT imaging applications.

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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