Dr. Gabriel Arenas Profile

Dr. Gabriel Arenas

at Univ of Pennsylvania Perelman School of Medicine

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Author

Publications (2)

SPIE Journal Paper | 14 May 2024

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Christian Herz, Nicolas Vergnet, Sijie Tian, Abdullah Aly, Matthew Jolley, Nathanael Tran, Gabriel Arenas, Andras Lasso, Nadav Schwartz, Kathleen O’Neill, Paul Yushkevich, Alison Pouch

JMI, Vol. 11, Issue 03, 036001, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.3.036001

KEYWORDS: Deformation, Anatomy, Modeling, Image segmentation, 3D modeling, Human-machine interfaces, Design, Medical imaging, Biomedical applications, Shape analysis

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PurposeDeformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging.ApproachA UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D segmentation, followed by interactive 3D point placement and triangulation of the medial surface such that the desired branching configuration of the anatomical structure’s medial axis is achieved. Synthetic skeleton design was evaluated in five clinical applications. Compatibility of the synthetic skeletons with open-source software for deformable medial modeling was tested, and representational accuracy of the deformed medial models was evaluated.ResultsThree users designed synthetic skeletons of anatomies with various topologies: the placenta, aortic root wall, mitral valve, cardiac ventricles, and the uterus. The skeletons were compatible with skeleton-first and boundary-first software for deformable medial modeling. The fitted medial models achieved good representational accuracy with respect to the 3D segmentations from which the synthetic skeletons were generated.ConclusionsSynthetic skeleton design has been a practical challenge in leveraging deformable medial modeling for new clinical applications. This work demonstrates an open-source UI for user-friendly design of synthetic skeletons for anatomies with a wide range of topologies.

Proceedings Article | 2 April 2024 Presentation + Paper

FNPC-SAM: uncertainty-guided false negative/positive control for SAM on noisy medical images

Xing Yao, Han Liu, Dewei Hu, Daiwei Lu, Ange Lou, Hao Li, Ruining Deng, Gabriel Arenas, Baris Oguz, Nadav Schwartz, Brett Byram, Ipek Oguz

Proceedings Volume 12926, 1292602 (2024) https://doi.org/10.1117/12.3006867

KEYWORDS: Image segmentation, Medical imaging, Kidney, Ultrasonography, Monte Carlo methods, Performance modeling, Image enhancement, Data modeling, Reliability, Uncertainty analysis

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