arXiv subjects

Jared Vicory

Publications and source records attributed to Jared Vicory.

5 recordsLinked to original sources

arXiv2024

Fitting Skeletal Models via Graph-based Learning

Skeletonization is a popular shape analysis technique that models an object's interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming process requiring much manual parameter tuning. Recently, machine learning-based methods have shown promise for generating s-reps from object boundaries. In this work, we propose a new skeletonization method which leverages graph convolutional networks to produce skeletal representations (s-reps) from dense segmentation masks. The method is evaluated on both synthetic data and real hippocampus segmentations, achieving promising results and fast inference.

cs.CV↗

arXiv2024

Interior Object Geometry via Fitted Frames

We propose a means of computing fitted frames on the boundary and in the interior of objects and using them to provide the basis for producing geometric features from them that are not only alignment-free but most importantly can be made to correspond locally across a population of objects. We describe a representation targeted for anatomic objects which is designed to enable this strong locational correspondence within object populations and thus to provide powerful object statistics. It accomplishes this by understanding an object as the diffeomorphic deformation of the closure of the interior of an ellipsoid and by using a skeletal representation fitted throughout the deformation to produce a model of the target object, where the object is provided initially in the form of a boundary mesh. Via classification performance on hippocampi shape between individuals with a disorder vs. others, we compare our method to two state-of-theart methods for producing object representations that are intended to capture geometric correspondence across a population of objects and to yield geometric features useful for statistics, and we show notably improved classification performance by this new representation, which we call the evolutionary s-rep. The geometric features that are derived from each of the representations, especially via fitted frames, are discussed.

cs.CV↗

arXiv2024

Geodesic-based Predictive Shape Modeling of the Right Ventricle in Patients with Hypoplastic Left Heart Syndrome

Hypoplastic left heart syndrome (HLHS) is characterized by severe underdevelopment of left ventricle requiring staged surgical reconstruction (stages) to allow the right ventricle (RV) alone to support the circulation. In this setting changes in RV size and shape over time reflect adaptations to single-ventricle physiology, dysfunction of the associated tricuspid valve (TV), and are associated with circulatory failure. As such, an accurate prediction of the RV shape of a patient would inform understanding of both RV and TV failure, as well as clinical prognosis and associated decision making. We present a geodesic-based predictive shape modeling framework applied a cohort of RVs obtained from 15 HLHS patients at three individual time points. Reasonable predictions on stage 1 RV shapes can generated using pre-stage 1 RV shapes and two predictors from prior clinical and demographic measures. Our results demonstrate the future potential for a data-driven method to predict how the morphology of the RV of an individual patient will change in size and shape over time. Future studies will seek to expand the training sample size and integrate more comprehensive demographic and morphological data into the proposed predictive model.

physics.med-ph↗

arXiv2023

ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing

High resolution peripheral quantitative computed tomography (HR-pQCT) is an imaging technique capable of imaging trabecular bone in-vivo. HR-pQCT has a wide range of applications, primarily focused on bone to improve our understanding of musculoskeletal diseases, assess epidemiological associations, and evaluate the effects of pharmaceutical interventions. Processing HR-pQCT images has largely been supported using the scanner manufacturer scripting language (Image Processing Language, IPL, Scanco Medical). However, by expanding image processing workflows outside of the scanner manufacturer software environment, users have the flexibility to apply more advanced mathematical techniques and leverage modern software packages to improve image processing. The ORMIR_XCT Python package was developed to reimplement some existing IPL workflows and provide an open and reproducible package allowing for the development of advanced HR-pQCT data processing workflows.

q-bio.QM↗

arXiv2023

Skeletal Point Representations with Geometric Deep Learning

Skeletonization has been a popular shape analysis technique that models both the interior and exterior of an object. Existing template-based calculations of skeletal models from anatomical structures are a time-consuming manual process. Recently, learning-based methods have been used to extract skeletons from 3D shapes. In this work, we propose novel additional geometric terms for calculating skeletal structures of objects. The results are similar to traditional fitted s-reps but but are produced much more quickly. Evaluation on real clinical data shows that the learned model predicts accurate skeletal representations and shows the impact of proposed geometric losses along with using s-reps as weak supervision.

cs.CV↗