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  - 
TY  - CHAP
A1  - Blanke, Tobias
A1  - Schmidt, Katharina S.
A1  - Göttsche, Joachim
A1  - Döring, Bernd
A1  - Frisch, Jérôme
A1  - van Treeck, Christoph
ED  - Weidlich, Anke
ED  - Neumann, Dirk
ED  - Gust, Gunther
ED  - Staudt, Philipp
ED  - Schäfer, Mirko
T1  - Time series aggregation for energy system design: review and extension of modelling seasonal storages
T2  - Energy Informatics
N2  - Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.
KW  - Energy system
KW  - Renewable energy
KW  - Mixed integer linear programming (MILP)
KW  - Typical periods
KW  - Time-series aggregation
Y1  - 2022
U6  - http://dx.doi.org/10.1186/s42162-022-00208-5
SN  - 2520-8942
N1  - Proceedings of the 11th DACH+ Conference on Energy Informatics, 15-16 September 2022, Freiburg, Germany.
VL  - 5
IS  - 1, Article number: 17
SP  - 1
EP  - 14
PB  - Springer Nature
ER  -