Machine learning in medical imaging. MLMI (Workshop)最新文献_第9页

Deformation field correction for spatial normalization of PET images using a population-derived partial least squares model. 使用源自群体的偏最小二乘法模型对 PET 图像的空间归一化进行变形场校正。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-10581-9_25

Murat Bilgel, Aaron Carass, Susan M Resnick, Dean F Wong, Jerry L Prince

{"title":"Deformation field correction for spatial normalization of PET images using a population-derived partial least squares model.","authors":"Murat Bilgel, Aaron Carass, Susan M Resnick, Dean F Wong, Jerry L Prince","doi":"10.1007/978-3-319-10581-9_25","DOIUrl":"10.1007/978-3-319-10581-9_25","url":null,"abstract":"Spatial normalization of positron emission tomography (PET) images is essential for population studies, yet work on anatomically accurate PET-to-PET registration is limited. We present a method for the spatial normalization of PET images that improves their anatomical alignment based on a deformation correction model learned from structural image registration. To generate the model, we first create a population-based PET template with a corresponding structural image template. We register each PET image onto the PET template using deformable registration that consists of an affine step followed by a diffeomorphic mapping. Constraining the affine step to be the same as that obtained from the PET registration, we find the diffeomorphic mapping that will align the structural image with the structural template. We train partial least squares (PLS) regression models within small neighborhoods to relate the PET intensities and deformation fields obtained from the diffeomorphic mapping to the structural image deformation fields. The trained model can then be used to obtain more accurate registration of PET images to the PET template without the use of a structural image. A cross validation based evaluation on 79 subjects shows that our method yields more accurate alignment of the PET images compared to deformable PET-to-PET registration as revealed by 1) a visual examination of the deformed images, 2) a smaller error in the deformation fields, and 3) a greater overlap of the deformed anatomical labels with ground truth segmentations.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"8679 ","pages":"198-206"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222176/pdf/nihms637009.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32803934","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}

引用次数: 0

Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression. 小脑共济失调分类与功能评分回归的深度学习。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-10581-9_9

Zhen Yang, Shenghua Zhong, Aaron Carass, Sarah H Ying, Jerry L Prince

{"title":"Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression.","authors":"Zhen Yang, Shenghua Zhong, Aaron Carass, Sarah H Ying, Jerry L Prince","doi":"10.1007/978-3-319-10581-9_9","DOIUrl":"https://doi.org/10.1007/978-3-319-10581-9_9","url":null,"abstract":"Cerebellar ataxia is a progressive neuro-degenerative disease that has multiple genetic versions, each with a characteristic pattern of anatomical degeneration that yields distinctive motor and cognitive problems. Studying this pattern of degeneration can help with the diagnosis of disease subtypes, evaluation of disease stage, and treatment planning. In this work, we propose a learning framework using MR image data for discriminating a set of cerebellar ataxia types and predicting a disease related functional score. We address the difficulty in analyzing high-dimensional image data with limited training subjects by: 1) training weak classifiers/regressors on a set of image subdomains separately, and combining the weak classifier/regressor outputs to make the decision; 2) perturbing the image subdomain to increase the training samples; 3) using a deep learning technique called the stacked auto-encoder to develop highly representative feature vectors of the input data. Experiments show that our approach can reliably classify between one of four categories (healthy control and three types of ataxia), and predict the functional staging score for ataxia.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"8679 ","pages":"68-76"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-10581-9_9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32945877","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}

引用次数: 25

Persistent Reeb Graph Matching for Fast Brain Search. 持久Reeb图匹配快速脑搜索。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-10581-9_38

Yonggang Shi, Junning Li, Arthur W Toga

引用次数: 7

Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images. 用于CT前列腺图像边界检测和可变形分割的学习距离变换。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-10581-9_12

Yaozong Gao, Li Wang, Yeqin Shao, Dinggang Shen

{"title":"Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images.","authors":"Yaozong Gao, Li Wang, Yeqin Shao, Dinggang Shen","doi":"10.1007/978-3-319-10581-9_12","DOIUrl":"10.1007/978-3-319-10581-9_12","url":null,"abstract":"Segmenting the prostate from CT images is a critical step in the radio-therapy planning for prostate cancer. The segmentation accuracy could largely affect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians. In this paper, we propose a learning-based approach for boundary detection and deformable segmentation of the prostate. Our proposed method aims to learn a boundary distance transform, which maps an intensity image into a boundary distance map. To enforce the spatial consistency on the learned distance transform, we combine our approach with the auto-context model for iteratively refining the estimated distance map. After the refinement, the prostate boundaries can be readily detected by finding the valley in the distance map. In addition, the estimated distance map can also be used as a new external force for guiding the deformable segmentation. Specifically, to automatically segment the prostate, we integrate the estimated boundary distance map into a level set formulation. Experimental results on 73 CT planning images show that the proposed distance transform is more effective than the traditional classification-based method for driving the deformable segmentation. Also, our method can achieve more consistent segmentations than human raters, and more accurate results than the existing methods under comparison.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"8679 ","pages":"93-100"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097539/pdf/nihms942711.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36411059","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}

引用次数: 23

Subject Specific Sparse Dictionary Learning for Atlas based Brain MRI Segmentation. 基于Atlas的脑MRI分割的主题稀疏字典学习。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2014-01-01 DOI: 10.1007/978-3-319-10581-9_31

Snehashis Roy, Aaron Carass, Jerry L Prince, Dzung L Pham

{"title":"Subject Specific Sparse Dictionary Learning for Atlas based Brain MRI Segmentation.","authors":"Snehashis Roy, Aaron Carass, Jerry L Prince, Dzung L Pham","doi":"10.1007/978-3-319-10581-9_31","DOIUrl":"10.1007/978-3-319-10581-9_31","url":null,"abstract":"Quantitative measurements from segmentations of soft tissues from magnetic resonance images (MRI) of human brains provide important biomarkers for normal aging, as well as disease progression. In this paper, we propose a patch-based tissue classification method from MR images using sparse dictionary learning from an atlas. Unlike most atlas-based classification methods, deformable registration from the atlas to the subject is not required. An \"atlas\" consists of an MR image, its tissue probabilities, and the hard segmentation. The \"subject\" consists of the MR image and the corresponding affine registered atlas probabilities (or priors). A subject specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches. The same sparse combination is applied to the segmentation patches of the atlas to generate tissue memberships of the subject. The novel combination of prior probabilities in the example patches enables us to distinguish tissues having similar intensities but having different spatial location. We show that our method outperforms two state-of-the-art whole brain tissue segmentation methods. We experimented on 12 subjects having manual tissue delineations, obtaining mean Dice coefficients of 0:91 and 0:87 for cortical gray matter and cerebral white matter, respectively. In addition, experiments on subjects with ventriculomegaly shows significantly better segmentation using our approach than the competing methods.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"8679 ","pages":"248-255"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-10581-9_31","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32803935","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}

引用次数: 78

Hot Spots Conjecture and Its Application to Modeling Tubular Structures 热点猜想及其在管状结构建模中的应用

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2011-09-18 DOI: 10.1007/978-3-642-24319-6_28

M. Chung, Seongho Seo, N. Adluru, H. K. Vorperian

引用次数: 24

Learning Statistical Correlation of Prostate Deformations for Fast Registration. 学习前列腺变形的统计相关性快速注册。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2011-01-01 DOI: 10.1007/978-3-642-24319-6_1

Yonghong Shi, Shu Liao, Dinggang Shen

{"title":"Learning Statistical Correlation of Prostate Deformations for Fast Registration.","authors":"Yonghong Shi, Shu Liao, Dinggang Shen","doi":"10.1007/978-3-642-24319-6_1","DOIUrl":"https://doi.org/10.1007/978-3-642-24319-6_1","url":null,"abstract":"This paper presents a novel fast registration method for aligning the planning image onto each treatment image of a patient for adaptive radiation therapy of the prostate cancer. Specifically, an online correspondence interpolation method is presented to learn the statistical correlation of the deformations between prostate boundary and non-boundary regions from a population of training patients, as well as from the online-collected treatment images of the same patient. With this learned statistical correlation, the estimated boundary deformations can be used to rapidly predict regional deformations between prostates in the planning and treatment images. In particular, the population-based correlation can be initially used to interpolate the dense correspondences when the number of available treatment images from the current patient is small. With the acquisition of more treatment images from the current patient, the patient-specific information gradually plays a more important role to reflect the prostate shape changes of the current patient during the treatment. Eventually, only the patient-specific correlation is used to guide the regional correspondence prediction, once a sufficient number of treatment images have been acquired and segmented from the current patient. Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy compared with the thin plate spline (TPS) based interpolation approach.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"7009 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-642-24319-6_1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32717742","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}

引用次数: 1

Appearance Normalization of Histology Slides. 组织学切片的外观归一化。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2010-01-01 DOI: 10.1007/978-3-642-15948-0_8

Marc Niethammer, David Borland, J S Marron, John Woosley, Nancy E Thomas

引用次数: 17