Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings最新文献

A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism. 对有自闭症风险的婴儿杏仁核和海马亚区的纵向核磁共振成像研究。

Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Pub Date : 2019-10-01 Epub Date: 2019-11-14 DOI: 10.1007/978-3-030-35817-4_20

Guannan Li, Meng-Hsiang Chen, Gang Li, Di Wu, Chunfeng Lian, Quansen Sun, Dinggang Shen, Li Wang

{"title":"A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism.","authors":"Guannan Li, Meng-Hsiang Chen, Gang Li, Di Wu, Chunfeng Lian, Quansen Sun, Dinggang Shen, Li Wang","doi":"10.1007/978-3-030-35817-4_20","DOIUrl":"10.1007/978-3-030-35817-4_20","url":null,"abstract":"Currently, there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavioral observations at three or four years of age. Since intervention efforts may miss a critical developmental window after 2 years old, it is clinically significant to identify imaging-based biomarkers at an early stage for better intervention, before behavioral diagnostic signs of ASD typically arising. Previous studies on older children and young adults with ASD demonstrate altered developmental trajectories of the amygdala and hippocampus. However, our knowledge on their developmental trajectories in early postnatal stages remains very limited. In this paper, for the first time, we propose a volume-based analysis of the amygdala and hippocampal subfields of the infant subjects with risk of ASD at 6, 12, and 24 months of age. To address the challenge of low tissue contrast and small structural size of infant amygdala and hippocampal subfields, we propose a novel deep-learning approach, dilated-dense U-Net, to digitally segment the amygdala and hippocampal subfields in a longitudinal dataset, the National Database for Autism Research (NDAR). A volume-based analysis is then performed based on the segmentation results. Our study shows that the overgrowth of amygdala and cornu ammonis sectors (CA) 1-3 May start from 6 months of age, which may be related to the emergence of autistic spectrum disorder.","PeriodicalId":92901,"journal":{"name":"Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings","volume":"11849 ","pages":"164-171"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043018/pdf/nihms-1060321.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37683418","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

Graph Learning in Medical Imaging: First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings 医学成像中的图学习:第一届国际研讨会，GLMI 2019，与MICCAI 2019一起举行，中国深圳，2019年10月17日，会议录

Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Pub Date : 2019-01-01 DOI: 10.1007/978-3-030-35817-4

Zhengdong Wang, Biao Jie, Mi Wang, Chunxiang Feng, Wen Zhou, D. Shen, Mingxia Liu

引用次数: 0

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks. DeepBundle:使用图卷积神经网络的光纤束分割。

Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Pub Date : 2019-01-01 Epub Date: 2019-11-14 DOI: 10.1007/978-3-030-35817-4_11

Feihong Liu, Jun Feng, Geng Chen, Ye Wu, Yoonmi Hong, Pew-Thian Yap, Dinggang Shen

{"title":"DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks.","authors":"Feihong Liu, Jun Feng, Geng Chen, Ye Wu, Yoonmi Hong, Pew-Thian Yap, Dinggang Shen","doi":"10.1007/978-3-030-35817-4_11","DOIUrl":"https://doi.org/10.1007/978-3-030-35817-4_11","url":null,"abstract":"Parcellation of whole-brain tractography streamlines is an important step for tract-based analysis of brain white matter microstructure. Existing fiber parcellation approaches rely on accurate registration between an atlas and the tractograms of an individual, however, due to large individual differences, accurate registration is hard to guarantee in practice. To resolve this issue, we propose a novel deep learning method, called DeepBundle, for registration-free fiber parcellation. Our method utilizes graph convolution neural networks (GCNNs) to predict the parcellation label of each fiber tract. GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method. We evaluate DeepBundle using data from the Human Connectome Project. Experimental results demonstrate the advantages of DeepBundle and suggest that the geometric features extracted from each fiber tract can be used to effectively parcellate the fiber tracts.","PeriodicalId":92901,"journal":{"name":"Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings","volume":"11849 ","pages":"88-95"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8411944/pdf/nihms-1717207.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39387491","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: 14

Geometric Brain Surface Network For Brain Cortical Parcellation. 脑皮质分割的几何脑表面网络。

Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Pub Date : 2019-01-01 Epub Date: 2019-11-14 DOI: 10.1007/978-3-030-35817-4_15

Wen Zhang, Yalin Wang

{"title":"Geometric Brain Surface Network For Brain Cortical Parcellation.","authors":"Wen Zhang, Yalin Wang","doi":"10.1007/978-3-030-35817-4_15","DOIUrl":"https://doi.org/10.1007/978-3-030-35817-4_15","url":null,"abstract":"A large number of surface-based analyses on brain imaging data adopt some specific brain atlases to better assess structural and functional changes in one or more brain regions. In these analyses, it is necessary to obtain an anatomically correct surface parcellation scheme in an individual brain by referring to the given atlas. Traditional ways to accomplish this goal are through a designed surface-based registration or hand-crafted surface features, although both of them are time-consuming. A recent deep learning approach depends on a regular spherical parameterization of the mesh, which is computationally prohibitive in some cases and may also demand further post-processing to refine the network output. Therefore, an accurate and fully-automatic cortical surface parcellation scheme directly working on the original brain surfaces would be highly advantageous. In this study, we propose an end-to-end deep brain cortical parcellation network, called DBPN. Through intrinsic and extrinsic graph convolution kernels, DBPN dynamically deciphers neighborhood graph topology around each vertex and encodes the deciphered knowledge into node features. Eventually, a non-linear mapping between the node features and parcellation labels is constructed. Our model is a two-stage deep network which contains a coarse parcellation network with a U-shape structure and a refinement network to fine-tune the coarse results. We evaluate our model in a large public dataset and our work achieves superior performance than state-of-the-art baseline methods in both accuracy and efficiency.","PeriodicalId":92901,"journal":{"name":"Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings","volume":"11849 ","pages":"120-129"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048406/pdf/nihms-1050207.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38886628","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: 2