More Like this

1. Feature Preserving Octree‐Based Hexahedral Meshing

   https://doi.org/10.1111/cgf.13795  

   Gao, Xifeng; Shen, Hanxiao; Panozzo, Daniele (August 2019, Computer Graphics Forum)
2. Development of a User-Focused Hexahedral Meshing Software for Swept Surfaces

   https://doi.org/10.1109/IETC64455.2025.11039343  

   Emmertson, Hayden; Goates, Caleb; Shepherd, Kendrick M (May 2025, IEEE)

   Hexahedral meshing plays a critical role in high fidelity computational solid mechanics. However, many tools remain inaccessible due to the cost of purchase or a poor user experience. In this work, we describe efforts to facilitate a highquality user experience with a new hexahedral meshing software developed using topological sweeping operations. These efforts include integrating the tool with existing software for engineering design, preprocessing, meshing, and visualization; creating a Python-based application protocol interface (API) to allow for easier scripting and interactions without requiring experience in C++; creating a user manual to discuss how to operate the software; and performing a simple case study on five potential users of various background about how to interact with the software. Feedback indicated the value of providing additional background and examples and will help improve future work on the user experience of this software in order to ultimately create a meaningful tool for educational and industrial use. 

   more » « less
3. Automated hexahedral meshing of knee cartilage structures – application to data from the osteoarthritis initiative

   https://doi.org/10.1080/10255842.2017.1383984  

   Rodriguez-Vila, B.; Sánchez-González, P.; Oropesa, I.; Gomez, E. J.; Pierce, D. M. (October 2017, Computer Methods in Biomechanics and Biomedical Engineering)
4. Robust automatic hexahedral cartilage meshing framework enables population-based computational studies of the knee

   https://doi.org/10.3389/fbioe.2022.1059003  

   Gibbons, Kalin D.; Malbouby, Vahid; Alvarez, Oliver; Fitzpatrick, Clare K. (December 2022, Frontiers in Bioengineering and Biotechnology)

   Osteoarthritis of the knee is increasingly prevalent as our population ages, representing an increasing financial burden, and severely impacting quality of life. The invasiveness of in vivo procedures and the high cost of cadaveric studies has left computational tools uniquely suited to study knee biomechanics. Developments in deep learning have great potential for efficiently generating large-scale datasets to enable researchers to perform population-sized investigations, but the time and effort associated with producing robust hexahedral meshes has been a limiting factor in expanding finite element studies to encompass a population. Here we developed a fully automated pipeline capable of taking magnetic resonance knee images and producing a working finite element simulation. We trained an encoder-decoder convolutional neural network to perform semantic image segmentation on the Imorphics dataset provided through the Osteoarthritis Initiative. The Imorphics dataset contained 176 image sequences with varying levels of cartilage degradation. Starting from an open-source swept-extrusion meshing algorithm, we further developed this algorithm until it could produce high quality meshes for every sequence and we applied a template-mapping procedure to automatically place soft-tissue attachment points. The meshing algorithm produced simulation-ready meshes for all 176 sequences, regardless of the use of provided (manually reconstructed) or predicted (automatically generated) segmentation labels. The average time to mesh all bones and cartilage tissues was less than 2 min per knee on an AMD Ryzen 5600X processor, using a parallel pool of three workers for bone meshing, followed by a pool of four workers meshing the four cartilage tissues. Of the 176 sequences with provided segmentation labels, 86% of the resulting meshes completed a simulated flexion-extension activity. We used a reserved testing dataset of 28 sequences unseen during network training to produce simulations derived from predicted labels. We compared tibiofemoral contact mechanics between manual and automated reconstructions for the 24 pairs of successful finite element simulations from this set, resulting in mean root-mean-squared differences under 20% of their respective min-max norms. In combination with further advancements in deep learning, this framework represents a feasible pipeline to produce population sized finite element studies of the natural knee from subject-specific models. 

   more » « less
5. FULLY AUTOMATED, HEXAHEDRAL MESHING OF PATIENT-SPECIFIC CARTILAGE STRUCTURES: DATA FROM THE OSTEOARTHRITIS INITIATIVE

   Borja Rodriguez‑Vila, David M. (July 2017, SB3C2017 Summer Biomechanics, Bioengineering and Biotransport Conference June 21 – 24, Tucson, AZ, USA)