scholarly journals Segmenting Lung Fields in Serial Chest Radiographs Using Both Population and Patient-Specific Shape Statistics

2006 ◽  
pp. 83-91 ◽  
Author(s):  
Yonghong Shi ◽  
Feihu Qi ◽  
Zhong Xue ◽  
Kyoko Ito ◽  
Hidenori Matsuo ◽  
...  
Keyword(s):  
Chest Radiographs ◽  
Patient Specific ◽  
Shape Statistics ◽  
Specific Shape

Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-Based and Patient-Specific Shape Statistics

2008 ◽  
Vol 27 (4) ◽  
pp. 481-494 ◽  
Author(s):  
Yonghong Shi ◽  
Feihu Qi ◽  
Zhong Xue ◽  
Liya Chen ◽  
K. Ito ◽  
...  
Keyword(s):  
Population Based ◽  
Chest Radiographs ◽  
Patient Specific ◽  
Shape Statistics ◽  
Specific Shape

scholarly journals A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3628
Author(s):  
Yingqian Liu ◽  
Zhuangzhi Yan
Keyword(s):  
Lattice Boltzmann ◽  
Active Contour Model ◽  
Input Image ◽  
Lattice Boltzmann Model ◽  
Patient Specific ◽  
Initial Contour ◽  
Shape Prior ◽  
Low Contrast ◽  
Specific Shape ◽  
Low Efficiency

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.

scholarly journals Proof of Concept Study for the Design, Manufacturing, and Testing of a Patient-Specific Shape Memory Device for Treatment of Unicoronal Craniosynostosis

2018 ◽  
Vol 29 (1) ◽  
pp. 45-48 ◽  
Author(s):  
Alessandro Borghi ◽  
Will Rodgers ◽  
Silvia Schievano ◽  
Allan Ponniah ◽  
Owase Jeelani ◽  
...  
Keyword(s):  
Shape Memory ◽  
Memory Device ◽  
Patient Specific ◽  
Proof Of Concept ◽  
Concept Study ◽  
Specific Shape

scholarly journals Individual Optimization of the Insertion of a Preformed Cochlear Implant Electrode Array

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Thomas S. Rau ◽  
Thomas Lenarz ◽  
Omid Majdani
Keyword(s):  
Cochlear Implant ◽  
Rating Scale ◽  
Electrode Array ◽  
Anatomical Variations ◽  
Patient Specific ◽  
Electrode Arrays ◽  
Insertion Technique ◽  
Custom Made ◽  
Specific Shape ◽  
Insertion Process

Purpose. The aim of this study was to show that individual adjustment of the curling behaviour of a preformed cochlear implant (CI) electrode array to the patient-specific shape of the cochlea can improve the insertion process in terms of reduced risk of insertion trauma.Methods. Geometry and curling behaviour of preformed, commercially available electrode arrays were modelled. Additionally, the anatomy of each small, medium-sized, and large human cochlea was modelled to consider anatomical variations. Finally, using a custom-made simulation tool, three different insertion strategies (conventional Advanced Off-Stylet (AOS) insertion technique, an automated implementation of the AOS technique, and a manually optimized insertion process) were simulated and compared with respect to the risk of insertion-related trauma. The risk of trauma was evaluated using a newly developed “trauma risk” rating scale.Results. Using this simulation-based approach, it was shown that an individually optimized insertion procedure is advantageous compared with the AOS insertion technique.Conclusion. This finding leads to the conclusion that, in general, consideration of the specific curling behaviour of a CI electrode array is beneficial in terms of less traumatic insertion. Therefore, these results highlight an entirely novel aspect of clinical application of preformed perimodiolar electrode arrays in general.

Design, Manufacturing, and In Vitro Testing of a Patient-Specific Shape-Memory Expander for Nose Reconstruction With Forehead Flap Technique

2016 ◽  
Vol 27 (1) ◽  
pp. 188-190 ◽  
Author(s):  
Alessandro Borghi ◽  
Will Rodgers ◽  
Silvia Schievano ◽  
Allan Ponniah ◽  
Justine O’Hara ◽  
...  
Keyword(s):  
Shape Memory ◽  
Patient Specific ◽  
In Vitro Testing ◽  
Forehead Flap ◽  
Nose Reconstruction ◽  
Flap Technique ◽  
Specific Shape

Concept of patient-specific shape memory implants for the treatment of orbital floor fractures

2017 ◽  
Vol 21 (2) ◽  
pp. 179-185 ◽  
Author(s):  
Ronny Grunert ◽  
Maximilian Wagner ◽  
Christian Rotsch ◽  
Harald Essig ◽  
Susanna Posern ◽  
...  
Keyword(s):  
Shape Memory ◽  
Orbital Floor ◽  
Patient Specific ◽  
Orbital Floor Fractures ◽  
Specific Shape
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