## 4 Discussion
### Literature Analysis
#### 4.2.2 Description of Advanced Computerized Techniques for Image Processing Pre-Processing
This technique is usually part of the segmentation process itself. Its disadvantages are that it depends on the existence of a large enough patient dataset library and it cannot consider a situation which is not already known within the library. 3--Fitting of geometric primitives averages the different parts of bones by fitting the closest 3D geometrical figure. For example, a long bone diaphysis may be approximated to a cylinder and its base and head to a truncated cone and a barrel shape, respectively. The orientation of the bone is then derived from the known geometry of the fitted primitives. The main disadvantage is that, unlike with a long bone such as a metatarsal, it is more difficult to apply this method to bones with a more complex shape such as the talus or calcaneus. Depending on the proprietary mix of technologies used to obtain the 3D orientation of each bone, software can provide an automated report of chosen measurements, usually a set of basic metrics (such as the M1-M2 angle, sesamoid rotation angle (SRA) for the forefoot, sagittal and axial talus-M1 angle and hindfoot alignment angle or foot-ankle offset) and a customized set of measurements chosen by the user. Reports in the literature regarding the usefulness and efficiency of these systems agree on their celerity, resulting in important time gains compared to measurements by hand [52; 53]. However, they also report that they differ from the latter, raising the question of which is the gold standard [53]. They also report a relevant number of failures or aberrant measurements, which are thought to be due to cases of low bone density, where the density of soft tissues is close to that of the bone, which can 'blur' the picture for the software, or the presence of metalwork or traumatic sequala, which are in any case unusual situations which can be misinterpreted by the software. Another explanation may be insufficient training when deep learning-based Ais are used. However, there is generally positive feedback regarding these new generation tools, with good reliability and excellent reproducibility [52; 53; 55; 56; 88]. 2. Distance Mapping or 3D-JSW, surface and volume measurements  
This is the advanced computerized method which has been the most reported on in the literature, to the best of our knowledge. This tool is based on analyzing the surface-to-surface distance map within any given weight-bearing joint. The sum of all these point-by-point distances adds up to a 3D joint space width map, which explains why it has been dubbed 'distance mapping' [66; 67; 69], '3D joint space width' [65; 68; 89; 90] or a mix of both in the literature. Originally developed as way to visualize regions of bony approximation, authors presented ways to also use it as a quantification tool for such conditions as PCFD or cavovarus deformity, by averaging the distances in reproducible in pre-defined anatomical quadrants within the joints of interest (Figure 9). Other than classical usage of this tool to quantify the evolution of osteoarthritis joints, authors have reported its ability to detect minute or more specific changes in the onset of hindfoot deformity, in PCFD [66] or cavovarus [67] configurations. This may lead in the future to early detection and preventive intervention in a wide range of foot and ankle pathologies. It is important to take into account that this technique does not enable the visualization of cartilage itself, but only the space in which the cartilage is to be found. It will therefore correctly depict an absence of cartilage, but not an area where cartilage is augmented, and an area where the joint is distracted may incorrectly be considered as cartilage augmentation. However, weight-bearing capacity largely eliminates these downsides, which in any event are also true of conventional 2 DXR, while the analysis is not possible at all with conventional non-weight-bearing MDCT.