## 3 Knee FEA Workflow
### Pre-Processing
#### 3.1.1 Geometry  
Commonly, an experienced musculoskeletal radiologist segments medical images, identifying and contouring the tissues of interest (bone, cartilage, meniscus, ligaments, and tendons) to obtain their 3D representation. For that, information from magnetic resonance imaging (MRI) or computed tomography (CT) scans can be used. MRI is usually preferred over CT since it has superior soft-tissue contrast and no ionizing radiation is used [19; 41; 42; 43].  
Figure 2: Finite element analysis workflow. Pre-processing integrates preparatory information to define the model. Processing solves the governing equations behind the physical interactions aided by computational tools. Post-processing evaluates the simulation outputs using qualitative and quantitative methods. Verification and validation feeds back into the modeling process to ensure logical results that represent the realistic behavior of the problem in question and draw conclusions regarding the problem simulated. MRI—Magnetic resonance imaging, CT—computed tomography, SSM—statistical shape modeling, CAD—computer aided design, PG—proteoglycan, IMU—inertial measurement unit, EMG—electromyography, AI—artificial intelligence, PDE—partial differential equation, ACL—anterior cruciate ligament, KL—Kellgren-Lawrence score, MOAKS—MRI osteoarthritis knee score, WORMS—whole-organ magnetic resonance imaging score.  
Manual segmenting of an MRI dataset of the knee, using an 0.5 mm slice thickness, takes several hours [26; 44; 45; 46]. For the CT dataset, it takes even longer due to higher image resolution. Hence, the rapid generation of 3D geometries from medical images has received special attention not only for modeling purposes but for surgery planning and implant designing [44; 45; 46; 47; 48; 49; 50; 51]. For example, [PERSON] et al. [44] automated the segmentation of MRI images via AI. They used convolutional neural networks (CNNs) to rapidly contour tissues in 2D and 3D domains and used statistical shape modeling (SSM) to control zones with high variability in shape, such as those susceptible to osteophytes. The authors trained their custom software with manual segmentations from the MICCAI SKI10 challenge ([[https://ski10.grand-challenge.org/](https://ski10.grand-challenge.org/)]([https://ski10.grand-challenge.org/](https://ski10.grand-challenge.org/))) (accessed on 13 October 2021) and the Osteoarthritis Initiative (OAI) ([[https://nda.nih.gov/oai/](https://nda.nih.gov/oai/)]([https://nda.nih.gov/oai/](https://nda.nih.gov/oai/))) (accessed on 13 October 2021). The accuracy of the automated method was as good as that of human experts and reduced the time to one-sixth of a manual procedure. In another example, [PERSON] et al. [52] sped up the segmentation and analysis of menisci in healthy and osteoarthritic knees from the OAI database. They automated the segmentation process based on active shape modeling (ASM) and SSM. Comparisons revealed that the automated process identified variables useful for OA evaluations such as volume, tibial coverage, and menisci subluxation as well as manual methods did. In total, segmenting and assessing one patient took ~30 min, suggesting the use of this approach for large cohort studies.  
Once the geometry is ready, commonly in a shell stereolithography CAD format (.stl), researchers assign a volume to it, translating the shell geometry to a solid format (e.g.,.igs). This activity is done by using custom algorithms or CAD software including SolidWorks (SolidWorks Corporation, Waltham, MA, USA), CATIA (Dassault Systemes, Velizy-Villacoublay, France), and Rhinoceros (Robert McNeel & Associates, Seattle, WA, USA).