Bayesian and grAphical models for biomedical imaging : first International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014 ; revised selected papers. BAMBI (Workshop) (1st : 2014 : Cambridge, Mass.)最新文献

英文中文

Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies. 在遗传研究中发现成像表型的球形主题模型

Bayesian and grAphical models for biomedical imaging : first International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014 ; revised selected papers. BAMBI (Workshop) (1st : 2014 : Cambridge, Mass.)

Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-12289-2_10

Kayhan N Batmanghelich, Michael Cho, Raul San Jose, Polina Golland

In this paper, we use Spherical Topic Models to discover the latent structure of lung disease. This method can be widely employed when a measurement for each subject is provided as a normalized histogram of relevant features. In this paper, the resulting descriptors are used as phenotypes to identify genetic markers associated with the Chronic Obstructive Pulmonary Disease (COPD). Features extracted from images capture the heterogeneity of the disease and therefore promise to improve detection of relevant genetic variants in Genome Wide Association Studies (GWAS). Our generative model is based on normalized histograms of image intensity of each subject and it can be readily extended to other forms of features as long as they are provided as normalized histograms. The resulting algorithm represents the intensity distribution as a combination of meaningful latent factors and mixing co-efficients that can be used for genetic association analysis. This approach is motivated by a clinical hypothesis that COPD symptoms are caused by multiple coexisting disease processes. Our experiments show that the new features enhance the previously detected signal on chromosome 15 with respect to standard respiratory and imaging measurements.

在本文中，我们使用球形主题模型来发现肺病的潜在结构。如果每个受试者的测量结果都是相关特征的归一化直方图，那么这种方法就能得到广泛应用。在本文中，所得到的描述符被用作表型来识别与慢性阻塞性肺病（COPD）相关的遗传标记。从图像中提取的特征捕捉到了疾病的异质性，因此有望改善全基因组关联研究（GWAS）中相关基因变异的检测。我们的生成模型以每个受试者图像强度的归一化直方图为基础，只要以归一化直方图的形式提供，就能很容易地扩展到其他形式的特征。由此产生的算法将强度分布表示为有意义的潜在因子和混合协系数的组合，可用于遗传关联分析。这种方法的动机来自于一个临床假设，即慢性阻塞性肺病的症状是由多种并存的疾病过程引起的。我们的实验表明，相对于标准的呼吸和成像测量，新特征增强了之前检测到的 15 号染色体上的信号。

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引用次数: 0

A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging. 用贝叶斯方法从有限扩散加权成像中识别舌间指肌。

Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-12289-2_2

Chuyang Ye, Aaron Carass, Emi Murano, Maureen Stone, Jerry L Prince

Fiber tracking in crossing regions is a well known issue in diffusion tensor imaging (DTI). Multi-tensor models have been proposed to cope with the issue. However, in cases where only a limited number of gradient directions can be acquired, for example in the tongue, the multi-tensor models fail to resolve the crossing correctly due to insufficient information. In this work, we address this challenge by using a fixed tensor basis and incorporating prior directional knowledge. Within a maximum a posteriori (MAP) framework, sparsity of the basis and prior directional knowledge are incorporated in the prior distribution, and data fidelity is encoded in the likelihood term. An objective function can then be obtained and solved using a noise-aware weighted ℓ₁-norm minimization. Experiments on a digital phantom and in vivo tongue diffusion data demonstrate that the proposed method is able to resolve crossing fibers with limited gradient directions.

光纤在交叉区域的跟踪是扩散张量成像(DTI)中一个众所周知的问题。多张量模型被提出来解决这个问题。然而，在只能获得有限数量的梯度方向的情况下，例如在舌头中，由于信息不足，多张量模型不能正确地解决交叉。在这项工作中，我们通过使用固定的张量基并结合先前的定向知识来解决这一挑战。在最大后验(MAP)框架中，先验分布中包含了基础的稀疏性和先验方向知识，并在似然项中编码了数据保真度。然后可以使用噪声感知加权1-范数最小化来获得和求解目标函数。实验结果表明，该方法能够分辨出梯度方向有限的交叉纤维。

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引用次数: 6

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