TY  - JOUR
A1  - Kahmann, Stephanie L.
A1  - Rausch, Valentin
A1  - Plümer, Jonathan
A1  - Müller, Lars P.
A1  - Pieper, Martin
A1  - Wegmann, Kilian
T1  - The automized fracture edge detection and generation of three-dimensional fracture probability heat maps
JF  - Medical Engineering & Physics
N2  - With proven impact of statistical fracture analysis on fracture classifications, it is desirable to minimize the manual work and to maximize repeatability of this approach. We address this with an algorithm that reduces the manual effort to segmentation, fragment identification and reduction. The fracture edge detection and heat map generation are performed automatically. With the same input, the algorithm always delivers the same output. The tool transforms one intact template consecutively onto each fractured specimen by linear least square optimization, detects the fragment edges in the template and then superimposes them to generate a fracture probability heat map.

We hypothesized that the algorithm runs faster than the manual evaluation and with low (< 5 mm) deviation. We tested the hypothesis in 10 fractured proximal humeri and found that it performs with good accuracy (2.5 mm ± 2.4 mm averaged Euclidean distance) and speed (23 times faster). When applied to a distal humerus, a tibia plateau, and a scaphoid fracture, the run times were low (1–2 min), and the detected edges correct by visual judgement. In the geometrically complex acetabulum, at a run time of 78 min some outliers were considered acceptable. An automatically generated fracture probability heat map based on 50 proximal humerus fractures matches the areas of high risk of fracture reported in medical literature.

Such automation of the fracture analysis method is advantageous and could be extended to reduce the manual effort even further.
KW  - Fracture classification
KW  - Shoulder
KW  - Probability distribution mapping
KW  - Morphing
KW  - Imaging
Y1  - 2022
SN  - 1350-4533
VL  - 2022
IS  - 110
PB  - Elsevier
CY  - Amsterdam
ER  -