Kayhan N Batmanghelich, Michael Cho, Raul San Jose, Polina Golland
{"title":"Spherical Topic Models for Imaging Phenotype Discovery in Genetic Studies.","authors":"Kayhan N Batmanghelich, Michael Cho, Raul San Jose, Polina Golland","doi":"10.1007/978-3-319-12289-2_10","DOIUrl":"10.1007/978-3-319-12289-2_10","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":90796,"journal":{"name":"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.)","volume":"8677 ","pages":"107-117"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4337963/pdf/nihms637936.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33415065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chuyang Ye, Aaron Carass, Emi Murano, Maureen Stone, Jerry L Prince
{"title":"A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Weighted Imaging.","authors":"Chuyang Ye, Aaron Carass, Emi Murano, Maureen Stone, Jerry L Prince","doi":"10.1007/978-3-319-12289-2_2","DOIUrl":"https://doi.org/10.1007/978-3-319-12289-2_2","url":null,"abstract":"<p><p>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 <i>ℓ</i><sub>1</sub>-norm minimization. Experiments on a digital phantom and <i>in vivo</i> tongue diffusion data demonstrate that the proposed method is able to resolve crossing fibers with limited gradient directions.</p>","PeriodicalId":90796,"journal":{"name":"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.)","volume":"8677 ","pages":"13-24"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-12289-2_2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33003519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}