Automated bony region identification using artificial neural networks: reliability and validation measurements

Gassman, Esther E.; Powell, Stephanie; Kallemeyn, Nicole A.; DeVries, Nicole A.; Shivanna, Kiran H.; Magnotta, Vincent A.; Ramme, Austin J.; Adams, Brian D.; Grosland, Nicole M.

doi:10.1007/s00256-007-0434-z

Cited by 18 publications

(20 citation statements)

References 38 publications

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“…Researchers have optimized bone segmentation using various techniques for the acetabulum (2), femoral head (2, 3), cranium (3), pelvis (3, 4), carpal bones (1), mandible (3), vertebrae (5) ribs (6), and various other regions (3, 7–12). Our laboratory has also developed a segmentation technique using an artificial neural network for the phalanx bones of the hand (13). …”

Section: Introductionmentioning

confidence: 99%

Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

et al. 2008

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Medical imaging technologies have allowed for in vivo exploration and evaluation of the human musculoskeletal system. Three-dimensional bone models generated using image segmentation techniques provide a means to optimize individualized orthopaedic surgical procedures using engineering analyses. However, many of the current segmentation techniques are not clinically practical due to the required time and human intervention. As a proof of concept, we demonstrate the use of an expectation maximization (EM) algorithm to segment the hand phalanx bones, and hypothesize that this semi-automated technique will improve on the efficiency while providing similar definitions as compared to a manual rater. Our results show a relative overlap of the proximal, middle, and distal phalanx bones of 0.83, 0.79, and 0.72 for the EM technique when compared to validated manual segmentations. The EM segmentations were also compared to 3D surface scans of the cadaveric specimens, which resulted in distance maps showing an average distance for the proximal, middle, and distal phalanx bones of 0.45, 0.46, and 0.51 mm, respectively. The EM segmentation improved on the segmentation speed of the manual techniques by a factor of eight. Overall, the manual segmentations had greater relative overlap metric values, which suggests that the manual segmentations are a better fit to the actual surface of the bone. As shown by the comparison to the bone surface scans, the EM technique provides a similar representation of the anatomic structure and offers an increase in efficiency that could help to reduce the time needed for defining anatomical structures from CT scans.

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Section: Introductionmentioning

confidence: 99%

Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

et al. 2008

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“…Moreover, the nature of the discretization influences the quality of the numerical solution (i.e., by the shape of the elements and the accurate approximation of the domain). An accurate approximation of the domain of interest depends not only on the mesh refinement[25], as determined by a convergence study, but on the accuracy of the method(s) used to segment the image datasets[26,27]. Both play a crucial role in the development of a valid mesh.…”

Section: Discussionmentioning

confidence: 99%

IA-FEMesh: An open-source, interactive, multiblock approach to anatomic finite element model development

Grosland

Shivanna

Magnotta

et al. 2009

Computer Methods and Programs in Biomedicine

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Finite element (FE) analysis is a valuable tool in musculoskeletal research. The demands associated with mesh development, however, often prove daunting. In an effort to facilitate anatomic FE model development we have developed an open source software toolkit (IA-FEMesh). IA-FEMesh employs a multiblock meshing scheme aimed at hexahedral mesh generation. An emphasis has been placed on making the tools interactive, in an effort to create a user friendly environment. The goal is to provide an efficient and reliable method for model development, visualization, and mesh quality evaluation. While these tools have been developed, initially, in the context of skeletal structures they can be applied to countless applications.

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“…The ability to define geometrically accurate representations of bony structures has previously been studied by DeVries et al[21] While defining the phalanx bones of the hand, good agreement was found between manual raters (Jaccard metric = 0.91) and physical laser scans of the same specimens (surface distance = 0.20mm). To facilitate the creation of the anatomic models both semi-automated techniques such as the expectation-maximization algorithms [16] as well as artificial neural networks (ANNs)[17] have been explored. In this paper, the BRAINS2 software was used to manually segment the regions of interest[22–25].…”

Section: Surgical Simulation Techniquesmentioning

confidence: 99%

“…In pursuit of making patient-specific modeling a reality, we have made advancements in automating the patient-specific bony geometry definitions from CT and/or MR image datasets [15–17] and toward easing the development of corresponding patient-specific finite element (FE) mesh definitions via a custom-written software package, IA-FEMesh[18]. Our goal is to further advance these efforts by developing tools to simulate a variety of surgical procedures, thereby interactively incorporating implants into such models.…”

Section: Introductionmentioning

confidence: 99%

Toward the Development of Virtual Surgical Tools to Aid Orthopaedic FE Analyses

Tadepalli

Shivanna

Magnotta

et al. 2009

EURASIP J. Adv. Signal Process.

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Computational models of joint anatomy and function provide a means for biomechanists, physicians, and physical therapists to understand the effects of repetitive motion, acute injury, and degenerative diseases. Finite element models, for example, may be used to predict the outcome of a surgical intervention or to improve the design of prosthetic implants. Countless models have been developed over the years to address a myriad of orthopaedic procedures. Unfortunately, few studies have incorporated patient-specific models. Historically, baseline anatomic models have been used due to the demands associated with model development. Moreover, surgical simulations impose additional modeling challenges. Current meshing practices do not readily accommodate the inclusion of implants. Our goal is to develop a suite of tools (virtual instruments and guides) which enable surgical procedures to be readily simulated and to facilitate the development of all-hexahedral finite element mesh definitions.

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Automated bony region identification using artificial neural networks: reliability and validation measurements

Cited by 18 publications

References 38 publications

Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

IA-FEMesh: An open-source, interactive, multiblock approach to anatomic finite element model development

Toward the Development of Virtual Surgical Tools to Aid Orthopaedic FE Analyses

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