scholarly journals Multi-object Segmentation of Head Bones

2009 ◽  
Author(s):  
Dagmar Kainmueller ◽  
Hans Lamecker ◽  
Heiko Seim ◽  
Stefan Zachow
Keyword(s):  
Image Data ◽  
Special Focus ◽  
Heuristic Model ◽  
3D Segmentation ◽  
Mandibular Bone ◽  
Shape Models ◽  
Statistical Shape ◽  
Fully Automatic ◽  
Ct Data ◽  
Manual Correction

We present a fully automatic method for 3D segmentation of the mandibular bone from CT data. The method includes an adaptation of statistical shape models of the mandible, the skull base and the midfacial bones, followed by a simultaneous graph-based optimization of adjacent deformable models. The adaptation of the models to the image data is performed according to a heuristic model of the typical intensity distribution in the vincinity of the bone boundary, with special focus on an accurate discrimination of adjacent bones in joint regions. An evaluation of our method based on 18 CT scans shows that a manual correction of the automatic segmentations is not necessary in approx. 60% of the axial slices that contain the mandible.

scholarly journals A New Deformation Pose Estimation Algorithm for the Fully Automatic Design of Patient Specific Knee Prosthesis: Preliminary Results

10.29007/v8d7 ◽  
2020 ◽  
Author(s):  
Charles Garraud ◽  
Arnaud Clavé ◽  
Jérôme Ogor ◽  
Eric Stindel ◽  
Guillaume Dardenne
Keyword(s):  
Knee Prosthesis ◽  
Synthetic Data ◽  
Estimation Algorithm ◽  
Source Model ◽  
Patient Specific ◽  
Shape Models ◽  
Statistical Shape ◽  
Fully Automatic ◽  
The Mean ◽  
Partial View

In the context of automatic landmarks localization with statistical shape models for the design of customized TKA prosthesis, the first step consists of registering a model, represented by the mean mesh of some healthy femoral bones, towards the segmented femur of the patient. The most complex aspect of the mesh-to-mesh correspondence in this case lies in the fact the source (model) and the target mesh can differ largely (partial view of the femur, anatomy that lies away from the mean) which makes common correspondence approaches inefficient. In this paper, we introduce a contribution to an algorithm from the field of object recognition that produces a reliable registration. By adding the concept of global deformability in the algorithm, we are able to improve the precision of the algorithm (mean mesh-to-mesh distance improved from 2.77mm to 0.79mm) and its robustness to anatomy far off the mean (better standard deviation and Hausdorff distance) on synthetic data . The next step will be to assess it in its application field i.e. the automatic localization of knee landmarks for the design of patient-specific knee prosthesis.

scholarly journals Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm

Spinal Cord ◽  
2020 ◽  
Vol 58 (7) ◽  
pp. 811-820 ◽  
Author(s):  
Sahar Sabaghian ◽  
Hamed Dehghani ◽  
Seyed Amir Hossein Batouli ◽  
Ali Khatibi ◽  
Mohammad Ali Oghabian
Keyword(s):  
Spinal Cord ◽  
Clustering Algorithm ◽  
3D Segmentation ◽  
Vertebral Canal ◽  
Fully Automatic

A framework for the merging of pre-existing and correspondenceless 3D statistical shape models

2014 ◽  
Vol 18 (7) ◽  
pp. 1044-1058 ◽  
Author(s):  
Marco Pereañez ◽  
Karim Lekadir ◽  
Constantine Butakoff ◽  
Corné Hoogendoorn ◽  
Alejandro F. Frangi
Keyword(s):  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape
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