神经网络(Neural Networks)是世界上三个最古老的神经建模学会的档案期刊:国际神经网络学会(INNS)、欧洲神经网络学会(ENNS)和日本神经网络学会(JNNS)。神经网络提供了一个论坛,以发展和培育一个国际社会的学者和实践者感兴趣的所有方面的神经网络和相关方法的计算智能。神经网络欢迎高质量论文的提交,有助于全面的神经网络研究,从行为和大脑建模,学习算法,通过数学和计算分析,系统的工程和技术应用,大量使用神经网络的概念和技术。这一独特而广泛的范围促进了生物和技术研究之间的思想交流,并有助于促进对生物启发的计算智能感兴趣的跨学科社区的发展。因此,神经网络编委会代表的专家领域包括心理学,神经生物学,计算机科学,工程,数学,物理。该杂志发表文章、信件和评论以及给编辑的信件、社论、时事、软件调查和专利信息。文章发表在五个部分之一:认知科学,神经科学,学习系统,数学和计算分析、工程和应用。 官网地址:http://dblp.uni-trier.de/db/journals/nn/

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Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This work adresses that need by providing a novel automatic initialization, which enables end to end registration. First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays. A domain randomization scheme is proposed to enable the network to overcome the challenge of being trained purely on simulated data and run inference on real Xrays. Then, for each patient CT, a patient-specific landmark extraction scheme is used. It is based on backprojecting and clustering the previously trained networks predictions on a set of simulated X-rays. Next, the network is retrained to detect the new landmarks. Finally the combination of network and 3D landmark locations is used to compute the initialization using a perspective-n-point algorithm. During the computation of the pose, a weighting scheme is introduced to incorporate the confidence of the network in detecting the landmarks. The algorithm is evaluated on the pelvis using both real and simulated x-rays. The mean (+-standard deviation) target registration error in millimetres is 4.1 +- 4.3 for simulated X-rays with a success rate of 92% and 4.2 +- 3.9 for real X-rays with a success rate of 86.8%, where a success is defined as a translation error of less than 30mm.

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Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This work adresses that need by providing a novel automatic initialization, which enables end to end registration. First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays. A domain randomization scheme is proposed to enable the network to overcome the challenge of being trained purely on simulated data and run inference on real Xrays. Then, for each patient CT, a patient-specific landmark extraction scheme is used. It is based on backprojecting and clustering the previously trained networks predictions on a set of simulated X-rays. Next, the network is retrained to detect the new landmarks. Finally the combination of network and 3D landmark locations is used to compute the initialization using a perspective-n-point algorithm. During the computation of the pose, a weighting scheme is introduced to incorporate the confidence of the network in detecting the landmarks. The algorithm is evaluated on the pelvis using both real and simulated x-rays. The mean (+-standard deviation) target registration error in millimetres is 4.1 +- 4.3 for simulated X-rays with a success rate of 92% and 4.2 +- 3.9 for real X-rays with a success rate of 86.8%, where a success is defined as a translation error of less than 30mm.

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