Partition-based optimization model for generative anatomy modeling language (POM-GAML)
Abstract Background This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g....
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doaj-6adb07f9a80440339f11c9b9e4c531b92020-11-25T02:56:33ZengBMCBMC Bioinformatics1471-21052019-03-0120S29911410.1186/s12859-019-2626-7Partition-based optimization model for generative anatomy modeling language (POM-GAML)Doga Demirel0Berk Cetinsaya1Tansel Halic2Sinan Kockara3Shahryar Ahmadi4Department of Computer Science, University of Arkansas at Little RockDepartment of Computer Science, University of Arkansas at Little RockDepartment of Computer Science, University of Central ArkansasDepartment of Computer Science, University of Central ArkansasDepartment of Orthopedic Surgery, University of Arkansas for Medical SciencesAbstract Background This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g. joints). This integrated non-linear optimization model requires the exponential execution time. However, our approach effectively computes the solution for non-linear optimization model and reduces computation time from exponential to linear time. This is achieved by grouping the 3D geometric constraints into communities. Methods Various community detection algorithms (k-means clustering, Clauset Newman Moore, and Density-Based Spatial Clustering of Applications with Noise) were used to find communities and partition the non-linear optimization problem into sub-problems. GAML was used to create a case study for 3D shoulder model to benchmark our approach with up to 5000 constraints. Results Our results show that the computation time was reduced from exponential time to linear time and the error rate between the partitioned and non-partitioned approach decreases with the increasing number of constraints. For the largest constraint set (5000 constraints), speed up was over 2689-fold whereas error was computed as low as 2.2%. Conclusion This study presents a novel approach to group anatomical constraints in 3D human shoulder model using community detection algorithms. A case study for 3D modeling for shoulder models developed for arthroscopic rotator cuff simulation was presented. Our results significantly reduced the computation time in conjunction with a decrease in error using constrained optimization by linear approximation, non-linear optimization solver.http://link.springer.com/article/10.1186/s12859-019-2626-7Modeling language for human anatomyArthroscopic rotator cuffVirtual human anatomyNonlinear programmingPartition-based optimization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Doga Demirel Berk Cetinsaya Tansel Halic Sinan Kockara Shahryar Ahmadi |
spellingShingle |
Doga Demirel Berk Cetinsaya Tansel Halic Sinan Kockara Shahryar Ahmadi Partition-based optimization model for generative anatomy modeling language (POM-GAML) BMC Bioinformatics Modeling language for human anatomy Arthroscopic rotator cuff Virtual human anatomy Nonlinear programming Partition-based optimization |
author_facet |
Doga Demirel Berk Cetinsaya Tansel Halic Sinan Kockara Shahryar Ahmadi |
author_sort |
Doga Demirel |
title |
Partition-based optimization model for generative anatomy modeling language (POM-GAML) |
title_short |
Partition-based optimization model for generative anatomy modeling language (POM-GAML) |
title_full |
Partition-based optimization model for generative anatomy modeling language (POM-GAML) |
title_fullStr |
Partition-based optimization model for generative anatomy modeling language (POM-GAML) |
title_full_unstemmed |
Partition-based optimization model for generative anatomy modeling language (POM-GAML) |
title_sort |
partition-based optimization model for generative anatomy modeling language (pom-gaml) |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2019-03-01 |
description |
Abstract Background This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g. joints). This integrated non-linear optimization model requires the exponential execution time. However, our approach effectively computes the solution for non-linear optimization model and reduces computation time from exponential to linear time. This is achieved by grouping the 3D geometric constraints into communities. Methods Various community detection algorithms (k-means clustering, Clauset Newman Moore, and Density-Based Spatial Clustering of Applications with Noise) were used to find communities and partition the non-linear optimization problem into sub-problems. GAML was used to create a case study for 3D shoulder model to benchmark our approach with up to 5000 constraints. Results Our results show that the computation time was reduced from exponential time to linear time and the error rate between the partitioned and non-partitioned approach decreases with the increasing number of constraints. For the largest constraint set (5000 constraints), speed up was over 2689-fold whereas error was computed as low as 2.2%. Conclusion This study presents a novel approach to group anatomical constraints in 3D human shoulder model using community detection algorithms. A case study for 3D modeling for shoulder models developed for arthroscopic rotator cuff simulation was presented. Our results significantly reduced the computation time in conjunction with a decrease in error using constrained optimization by linear approximation, non-linear optimization solver. |
topic |
Modeling language for human anatomy Arthroscopic rotator cuff Virtual human anatomy Nonlinear programming Partition-based optimization |
url |
http://link.springer.com/article/10.1186/s12859-019-2626-7 |
work_keys_str_mv |
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