## 4 Discussion
### Literature Analysis
#### 4.2.2 Description of Advanced Computerized Techniques for Image Processing Pre-Processing
However, it remains a measurement of hindfoot alignment and does not assess sub-level type deformities: it is not a diagnostic tool. Other techniques based on the traditional 2 DXR-MDCT literature remain for now the only tools to allow for this [14; 83]. Figure 7: Example of a foot-ankle offset (FAO) measurement report using Talas® system (CurvebeamAI, Hatfield, PA, USA). FAO is a 3D biometric hindfoot alignment measurement which is calculated as a percentage after manual identification of four anatomical landmarks (the weight-bearing points of the first metatarsal (M1) and fifth metatarsal (M5), the calcaneus bones (C) and the center of the ankle joint (T)). Segmentation  
We identified automatic segmentation techniques as the basis of other recent innovations [84; 85]. It appears indispensable because the time required to perform manual segmentation is too much for clinical applications, confining potential innovations to the research area. The important word in 'automatic segmentation' is therefore'segmentation', but this also turns out to be the most complicated to achieve. Segmentation is the process by which individual bones are outlined and labelled. When segmentation is carried out manually, every single bone (28 in a single foot) must be manually outlined on every slice (up to 1000 per dataset), which amounts to 28,000 operations per foot, or 46,000 per bilateral scan. In practice, it generally takes 30 min to segment the hindfoot bones and 2 h to segment the whole foot and ankle [86]. It is therefore paramount that fast and reproducible automatic segmentation software become widely available. In that case, studies report that the same process can take a little as a few seconds [53]. The present review found four reported techniques or software solutions. These initially used automatic contouring based on contrast definition, evolving to more advanced techniques, ultimately aided by proprietary solutions. The two most reported in the literature are Bonelogics(Disior Oy, then Paragon28) and Cubeview(Curveabeam AI). Although much of their inner work remains undisclosed, the industry advertises that they rely on a combination of algorithms and artificial intelligence (AI) (i.e., to automatically identify bones, to reproduce surgical procedures such as osteotomies or arthrolpsties and to run automatic measurements in order to anticipate which gestures are needed to obtain the desired alignment). Throughout the process, it is reported that some of the reasons for the failure of the automatic segmentation are the presence of metalwork; poor bone quality or the presence of arthritic joints leading to areas of contact between bones; and the misinterpretation of two touching bones as a single structure [52]. One important step in the segmentation process is the smoothing of bone contours. This is the equivalent of noise reduction in digital photography: smoothing reduces the risk of two touching bones being misinterpreted as a single one but results in a (marginal) reduction of the quantity of data. The AI training process helps in dealing with the aforementioned contouring issues as well as learning to correctly label the bones. It makes more sense from a practical and industrial standpoint to build a system that can label the anatomy and then produce all relevant measurements, rather than build multiple systems to perform each measurement. It transpires that large amounts of data are necessary to efficiently train these Ais, which will need to be scientifically evaluated by independent entities on a regular basis until satisfactory performance is reached. There is unequivocal agreement on this amongst authors and stakeholders [15; 52; 53]. Post-Segmentation  
Once segmentation is achieved, a digital clone of the foot and ankle is created with all bones correctly labelled.