Interactive 3D editing tools for image segmentation

Kang, Yan; Engelke, Klaus; Kalender, Willi A.

doi:10.1016/j.media.2003.07.002

Cited by 83 publications

(53 citation statements)

References 9 publications

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“…Another advantage is the automatic determination of a neck coordinate system (NCS), relative to which the volumes of interest (VOIs) analyzed are located. (14,(35)(36)(37) The analysis is semiautomatic, with only occasional corrections by the operator being necessary. (37) In this study, Version 4.0 was used; the processing time for each femur was 15 to 20 minutes (Pentium 4, 1 GB).…”

Section: Image Analysismentioning

confidence: 99%

In vivo discrimination of hip fracture with quantitative computed tomography: Results from the prospective European Femur Fracture Study (EFFECT)

Bousson

Adams

Engelke³

et al. 2010

Journal of Bone and Mineral Research

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In assessing osteoporotic fractures of the proximal femur, the main objective of this in vivo case-control study was to evaluate the performance of quantitative computed tomography (QCT) and a dedicated 3D image analysis tool [Medical Image Analysis Framework-Femur option (MIAF-Femur)] in differentiating hip fracture and non-hip fracture subjects. One-hundred and seven women were recruited in the study, 47 women (mean age 81.6 years) with low-energy hip fractures and 60 female non-hip fracture control subjects (mean age 73.4 years). Bone mineral density (BMD) and geometric variables of cortical and trabecular bone in the femoral head and neck, trochanteric, and intertrochanteric regions and proximal shaft were assessed using QCT and MIAF-Femur. Areal BMD (aBMD) was assessed using dual-energy X-ray absorptiometry (DXA) in 96 (37 hip fracture and 59 non-hip fracture subjects) of the 107 patients. Logistic regressions were computed to extract the best discriminates of hip fracture, and area under the receiver characteristic operating curve (AUC) was calculated. Three logistic models that discriminated the occurrence of hip fracture with QCT variables were obtained (AUC ¼ 0.84). All three models combined one densitometric variable-a trabecular BMD (measured in the femoral head or in the trochanteric region)-and one geometric variable-a cortical thickness value (measured in the femoral neck or proximal shaft). The best discriminant using DXA variables was obtained with total femur aBMD (AUC ¼ 0.80, p ¼ .003). Results highlight a synergistic contribution of trabecular and cortical components in hip fracture risk and the utility of assessing QCT BMD of the femoral head for improved understanding and possible insights into prevention of hip fractures. ß

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Section: Image Analysismentioning

confidence: 99%

In vivo discrimination of hip fracture with quantitative computed tomography: Results from the prospective European Femur Fracture Study (EFFECT)

Bousson

Adams

Engelke³

et al. 2010

Journal of Bone and Mineral Research

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“…The most simple approaches are geometrical modeling tools which can be used to aid the manual construction of a 3D model, such as in [4,5]. More recently, Heckel et al have proposed a variational interpolation framework to implement near real-time reconstruction of a 3D model from a set of contours [2].…”

Section: Introductionmentioning

confidence: 99%

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Barbosa

Heyde

Čikeš

et al. 2014

Computerized Medical Imaging and Graphics

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“…Automatic methods cannot guarantee perfect segmentations since the user is not directly involved in the segmentation process. It can thus be rather difficult to correct errors of automatic segmentation, necessitating a final quality assurance pass using some separate interactive segmentation editing tool [8,6].…”

Section: Introductionmentioning

confidence: 99%

Spotlight: Automated Confidence-Based User Guidance for Increasing Efficiency in Interactive 3D Image Segmentation

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Hamarneh

Abugharbieh

2011

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. We present Spotlight, an automated user guidance technique for improving quality and efficiency of interactive segmentation tasks. Spotlight augments interactive segmentation algorithms by automatically highlighting areas in need of attention to the user during the interaction phase. We employ a 3D Livewire algorithm as our base segmentation method where the user quickly provides a minimal initial contour seeding. The quality of the initial segmentation is then evaluated based on three different metrics that probe the contour edge strength, contour stability and object connectivity. The result of this evaluation is fed into a novel algorithm that autonomously suggests regions that require user intervention. Essentially, Spotlight flags potentially problematic image regions in a prioritized fashion based on an optimization process for improving the final 3D segmentation. We present a variety of qualitative and quantitative examples demonstrating Spotlight's intuitive use and proven utility in reducing user input by increasing automation.

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Interactive 3D editing tools for image segmentation

Cited by 83 publications

References 9 publications

In vivo discrimination of hip fracture with quantitative computed tomography: Results from the prospective European Femur Fracture Study (EFFECT)

In vivo discrimination of hip fracture with quantitative computed tomography: Results from the prospective European Femur Fracture Study (EFFECT)

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Spotlight: Automated Confidence-Based User Guidance for Increasing Efficiency in Interactive 3D Image Segmentation

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