Mastoid Cavity Dimensions and Shape: Method of Measurement and Virtual Fitting of Implantable Devices
Ophir HandzelHaobing WangJason FieringJeffrey T. BorensteinMark J. MescherErin E. Leary SwanBrian MurphyZhiqiang ChenMarcello PeppiWilliam F. SewellSharon G. KujawaMichael J. McKenna
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Abstract:
Temporal bone implants can be used to electrically stimulate the auditory nerve, to amplify sound, to deliver drugs to the inner ear and potentially for other future applications. The implants require storage space and access to the middle or inner ears. The most acceptable space is the cavity created by a canal wall up mastoidectomy. Detailed knowledge of the available space for implantation and pathways to access the middle and inner ears is necessary for the design of implants and successful implantation. Based on temporal bone CT scans a method for three-dimensional reconstruction of a virtual canal wall up mastoidectomy space is described. Using Amira® software the area to be removed during such surgery is marked on axial CT slices, and a three-dimensional model of that space is created. The average volume of 31 reconstructed models is 12.6 cm<sup>3</sup> with standard deviation of 3.69 cm<sup>3</sup>, ranging from 7.97 to 23.25 cm<sup>3</sup>. Critical distances were measured directly from the model and their averages were calculated: height 3.69 cm, depth 2.43 cm, length above the external auditory canal (EAC) 4.45 cm and length posterior to EAC 3.16 cm. These linear measurements did not correlate well with volume measurements. The shape of the models was variable to a significant extent making the prediction of successful implantation for a given design based on linear and volumetric measurement unreliable. Hence, to assure successful implantation, preoperative assessment should include a virtual fitting of an implant into the intended storage space. The above-mentioned three-dimensional models were exported from Amira to a Solidworks application where virtual fitting was performed. Our results are compared to other temporal bone implant virtual fitting studies. Virtual fitting has been suggested for other human applications.The algorithm GDBSCAN only needs two parameters.It can discover clusters of any shape.But it is very sensitive to the parameter Eps.This paper puts forward a kind of method to make sure the Eps,which is based on datagrid.The method can achieve better result on the case of data distributing unevenly and shapes of clusters reaching each other.
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We present a method to classify objects in video streams using a brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object classification is a challenging task where humans still significantly outperform machine learning algorithms due to their unique capabilities. We have implemented a system which achieves very promising performance in terms of recognition accuracy. Unfortunately, conducting more advanced experiments is very computationally demanding; some of the trials run on a standard CPU may take as long as several days for 960x540 video streams frames. Therefore we have decided to accelerate selected parts of the system using OpenCL. In particular, we seek to determine to what extent porting selected and computationally demanding parts of a core may speed up calculations. The classification accuracy of the system was examined through a series of experiments and the performance was given in terms of F1 score as a function of the number of columns, synapses, $min\_overlap$ and $winners\_set\_size$. The system achieves the highest F1 score of 0.95 and 0.91 for $min\_overlap=4$ and 256 synapses, respectively. We have also conduced a series of experiments with different hardware setups and measured CPU/GPU acceleration. The best kernel speed-up of 632x and 207x was reached for 256 synapses and 1024 columns. However, overall acceleration including transfer time was significantly lower and amounted to 6.5x and 3.2x for the same setup.
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This paper analyzed the DataGrid Web Server Control. DataGrid is one of the most popular control of ASP .NET which is used to render data to a Web page in tabular form. This paper provides two types of typical usage of DataGrid.
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