An articulated statistical shape model for accurate hip joint segmentation

Kainmueller, Dagmar; Lamecker, Hans; Zachow, Stefan; Hege, Hans-Christian

doi:10.1109/iembs.2009.5333269

Cited by 57 publications

(41 citation statements)

References 7 publications

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“…The deforming mesh is in green Fig. 4 Illustration of internal constraint points (CPs): The closest face (P 0 ; P 1 ) to the CP P is attracted by creating 2 forces f 1 and f 2 on P 0 and P 1 , respectively, whose calculation depends on P and its projection P ⊥ on the face successfully applied to segment a wide variety of structures (e.g., bone [12,20], liver [9], and bladder [2]). …”

Section: Multiuser Iterative Image Segmentationmentioning

confidence: 99%

Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

et al. 2010

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Telemedicine has evolved rapidly in recent years to enable unprecedented access to digital medical data, such as with networked image distribution/sharing and online (distant) collaborative diagnosis, largely due to the advances in telecommunication and multimedia technologies. However, interactive collaboration systems which control editing of an object among multiple users are often limited to a simple "locking" mechanism based on a conventional client/server architecture, where only one user edits the object which is located in a specific server, while all other users become viewers. Such systems fail to provide the needs of a modern day telemedicine applications that demand simultaneous editing of the medical data distributed in diverse local sites. In this study, we introduce a novel system for telemedicine applications, with its application to an interactive segmentation of volumetric medical images. We innovate by proposing a collaborative mechanism with a scalable data sharing architecture which makes users interactively edit on a single shared image scattered in local sites, thus enabling collaborative editing for, e.g., collaborative diagnosis, teaching, and training. We demonstrate our collaborative telemedicine mechanism with a prototype image edit- ing system developed and evaluated with a user case study. Our result suggests that the ability for collaborative editing in a telemedicine context can be of great benefit and hold promising potential for further research.

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Section: Multiuser Iterative Image Segmentationmentioning

confidence: 99%

Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

et al. 2010

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“…Various work on hip CT segmentation have been reported and consist of three main categories: intensity-based methods [1][2][3], statistical shape model (SSM) [4][5][6][7][8], and atlas-based approaches [9][10][11]. Kang et al [1] presented an intensity-based segmentation method with four-steps.…”

Section: Introductionmentioning

confidence: 99%

Fully Automatic Segmentation of Hip CT Images

Chu

Bai

et al. 2015

Computational Radiology for Orthopaedic Interventions

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Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively.

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“…This approach is beneficial only if relative transformations between individual objects are a statistical property of anatomy, which is, e.g., not the case for knee bending. Instead, we follow the approach presented for the hip joint in [5] and propose an articulated SSM (ASSM) of the knee, where we model knee joint posture explicitly as a combination of characteristic transformations [6]. With an evaluation on 40 MRI datasets, we show that our knee ASSM outperforms reconstruction based on separate SSMs.…”

Section: Introductionmentioning

confidence: 99%

An Articulated Statistical Shape Model of the Human Knee

Bindernagel

Kainmueller

Seim

et al. 2011

Bildverarbeitung Für Die Medizin 2011

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Abstract. In this work we present an articulated statistical shape model (ASSM) of the human knee. The model incorporates statistical shape variation plus explicit degrees of freedom that model physiological joint motion. We also present a strategy for segmentation of the knee joint from medical image data. We show the potential of the model via an evaluation on a set of 40 clinical MRI datasets with manual expert segmentations available.

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An articulated statistical shape model for accurate hip joint segmentation

Cited by 57 publications

References 7 publications

Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

Fully Automatic Segmentation of Hip CT Images

An Articulated Statistical Shape Model of the Human Knee

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