Machine learning in medical imaging. MLMI (Workshop)最新文献_第4页

Unsupervised Learning for Spherical Surface Registration. 球面配准的无监督学习。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-10-01 Epub Date: 2020-09-29 DOI: 10.1007/978-3-030-59861-7_38

Fenqiang Zhao, Zhengwang Wu, Li Wang, Weili Lin, Shunren Xia, Dinggang Shen, Gang Li

{"title":"Unsupervised Learning for Spherical Surface Registration.","authors":"Fenqiang Zhao, Zhengwang Wu, Li Wang, Weili Lin, Shunren Xia, Dinggang Shen, Gang Li","doi":"10.1007/978-3-030-59861-7_38","DOIUrl":"10.1007/978-3-030-59861-7_38","url":null,"abstract":"Current spherical surface registration methods achieve good performance on alignment and spatial normalization of cortical surfaces across individuals in neuroimaging analysis. However, they are computationally intensive, since they have to optimize an objective function independently for each pair of surfaces. In this paper, we present a fast learning-based algorithm that makes use of the recent development in spherical Convolutional Neural Networks (CNNs) for spherical cortical surface registration. Given a set of surface pairs without supervised information such as ground truth deformation fields or anatomical landmarks, we formulate the registration as a parametric function and learn its parameters by enforcing the feature similarity between one surface and the other one warped by the estimated deformation field using the function. Then, given a new pair of surfaces, we can quickly infer the spherical deformation field registering one surface to the other one. We model this parametric function using three orthogonal Spherical U-Nets and use spherical transform layers to warp the spherical surfaces, while imposing smoothness constraints on the deformation field. All the layers in the network are well-defined and differentiable, thus the parameters can be effectively learned. We show that our method achieves accurate cortical alignment results on 102 subjects, comparable to two state-of-the-art methods: Spherical Demons and MSM, while runs much faster.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"12436 ","pages":"373-383"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7871893/pdf/nihms-1666982.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25355086","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

A Deep Network for Joint Registration and Reconstruction of Images with Pathologies. 带病理图像联合配准与重建的深度网络。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-10-01 Epub Date: 2020-09-29 DOI: 10.1007/978-3-030-59861-7_35

Xu Han, Zhengyang Shen, Zhenlin Xu, Spyridon Bakas, Hamed Akbari, Michel Bilello, Christos Davatzikos, Marc Niethammer

{"title":"A Deep Network for Joint Registration and Reconstruction of Images with Pathologies.","authors":"Xu Han, Zhengyang Shen, Zhenlin Xu, Spyridon Bakas, Hamed Akbari, Michel Bilello, Christos Davatzikos, Marc Niethammer","doi":"10.1007/978-3-030-59861-7_35","DOIUrl":"10.1007/978-3-030-59861-7_35","url":null,"abstract":"Registration of images with pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. Moreover, mass effects as observed for brain tumors may displace tissue, creating larger deformations over time than what is observed in a healthy brain. Deep learning models have successfully been applied to image registration to offer dramatic speed up and to use surrogate information (e.g., segmentations) during training. However, existing approaches focus on learning registration models using images from healthy patients. They are therefore not designed for the registration of images with strong pathologies for example in the context of brain tumors, and traumatic brain injuries. In this work, we explore a deep learning approach to register images with brain tumors to an atlas. Our model learns an appearance mapping from images with tumors to the atlas, while simultaneously predicting the transformation to atlas space. Using separate decoders, the network disentangles the tumor mass effect from the reconstruction of quasi-normal images. Results on both synthetic and real brain tumor scans show that our approach outperforms cost function masking for registration to the atlas and that reconstructed quasi-normal images can be used for better longitudinal registrations.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"12436 ","pages":"342-352"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8354331/pdf/nihms-1684179.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39302644","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: 10

Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation. 无监督MRI均匀化:应用于儿童前视通路分割。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-10-01 Epub Date: 2020-09-29 DOI: 10.1007/978-3-030-59861-7_19

Carlos Tor-Diez, Antonio R Porras, Roger J Packer, Robert A Avery, Marius George Linguraru

{"title":"Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation.","authors":"Carlos Tor-Diez, Antonio R Porras, Roger J Packer, Robert A Avery, Marius George Linguraru","doi":"10.1007/978-3-030-59861-7_19","DOIUrl":"https://doi.org/10.1007/978-3-030-59861-7_19","url":null,"abstract":"Deep learning strategies have become ubiquitous optimization tools for medical image analysis. With the appropriate amount of data, these approaches outperform classic methodologies in a variety of image processing tasks. However, rare diseases and pediatric imaging often lack extensive data. Specially, MRI are uncommon because they require sedation in young children. Moreover, the lack of standardization in MRI protocols introduces a strong variability between different datasets. In this paper, we present a general deep learning architecture for MRI homogenization that also provides the segmentation map of an anatomical region of interest. Homogenization is achieved using an unsupervised architecture based on variational autoencoder with cycle generative adversarial networks, which learns a common space (i.e. a representation of the optimal imaging protocol) using an unpaired image-to-image translation network. The segmentation is simultaneously generated by a supervised learning strategy. We evaluated our method segmenting the challenging anterior visual pathway using three brain T1-weighted MRI datasets (variable protocols and vendors). Our method significantly outperformed a non-homogenized multi-protocol U-Net.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"12436 ","pages":"180-188"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317430/pdf/nihms-1723844.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39259034","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: 8

Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients. 通过估计黎曼姿态梯度扩展刚性2D/3D配准的捕获范围。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-10-01 Epub Date: 2020-09-29 DOI: 10.1007/978-3-030-59861-7_29

Wenhao Gu, Cong Gao, Robert Grupp, Javad Fotouhi, Mathias Unberath

{"title":"Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients.","authors":"Wenhao Gu, Cong Gao, Robert Grupp, Javad Fotouhi, Mathias Unberath","doi":"10.1007/978-3-030-59861-7_29","DOIUrl":"https://doi.org/10.1007/978-3-030-59861-7_29","url":null,"abstract":"Traditional intensity-based 2D/3D registration requires near-perfect initialization in order for image similarity metrics to yield meaningful updates of X-ray pose and reduce the likelihood of getting trapped in a local minimum. The conventional approaches strongly depend on image appearance rather than content, and therefore, fail in revealing large pose offsets that substantially alter the appearance of the same structure. We complement traditional similarity metrics with a convolutional neural network-based (CNN-based) registration solution that captures large-range pose relations by extracting both local and contextual information, yielding meaningful X-ray pose updates without the need for accurate initialization. To register a 2D X-ray image and a 3D CT scan, our CNN accepts a target X-ray image and a digitally reconstructed radiograph at the current pose estimate as input and iteratively outputs pose updates in the direction of the pose gradient on the Riemannian Manifold. Our approach integrates seamlessly with conventional image-based registration frameworks, where long-range relations are captured primarily by our CNN-based method while short-range offsets are recovered accurately with an image similarity-based method. On both synthetic and real X-ray images of the human pelvis, we demonstrate that the proposed method can successfully recover large rotational and translational offsets, irrespective of initialization.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"12436 ","pages":"281-291"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7605345/pdf/nihms-1639752.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38564151","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: 7

O-Net: An Overall Convolutional Network for Segmentation Tasks. O-Net:一个用于分割任务的整体卷积网络。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-10-01 Epub Date: 2020-09-29 DOI: 10.1007/978-3-030-59861-7_21

Omid Haji Maghsoudi, Aimilia Gastounioti, Lauren Pantalone, Christos Davatzikos, Spyridon Bakas, Despina Kontos

{"title":"O-Net: An Overall Convolutional Network for Segmentation Tasks.","authors":"Omid Haji Maghsoudi, Aimilia Gastounioti, Lauren Pantalone, Christos Davatzikos, Spyridon Bakas, Despina Kontos","doi":"10.1007/978-3-030-59861-7_21","DOIUrl":"https://doi.org/10.1007/978-3-030-59861-7_21","url":null,"abstract":"Convolutional neural networks (CNNs) have recently been popular for classification and segmentation through numerous network architectures offering a substantial performance improvement. Their value has been particularly appreciated in the domain of biomedical applications, where even a small improvement in the predicted segmented region (e.g., a malignancy) compared to the ground truth can potentially lead to better diagnosis or treatment planning. Here, we introduce a novel architecture, namely the Overall Convolutional Network (O-Net), which takes advantage of different pooling levels and convolutional layers to extract more deeper local and containing global context. Our quantitative results on 2D images from two distinct datasets show that O-Net can achieve a higher dice coefficient when compared to either a U-Net or a Pyramid Scene Parsing Net. We also look into the stability of results for training and validation sets which can show the robustness of model compared with new datasets. In addition to comparison to the decoder, we use different encoders including simple, VGG Net, and ResNet. The ResNet encoder could help to improve the results in most of the cases.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":"12436 ","pages":"199-209"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286447/pdf/nihms-1684028.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39202023","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: 3

Correction to: Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification 更正:从静息状态fMRI构建高阶动态功能连接网络用于脑痴呆识别

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-09-29 DOI: 10.1007/978-3-030-59861-7_69

Chunxiang Feng, Biao Jie, Xintao Ding, Daoqiang Zhang, Mingxia Liu

引用次数: 0

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity. 在递归神经网络中用于神经病理生理异质性建模的人口导向关注。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-01-01 Epub Date: 2020-09-29 DOI: 10.1007/978-3-030-59861-7_37

Nicha C Dvornek, Xiaoxiao Li, Juntang Zhuang, Pamela Ventola, James S Duncan

引用次数: 0

Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet. 使用GraphNet的单纯形回归增强结构连接性的功能性脑网络。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-01-01 Epub Date: 2020-09-29 DOI: 10.1007/978-3-030-59861-7_30

Mansu Kim, Jingxaun Bao, Kefei Liu, Bo-Yong Park, Hyunjin Park, Li Shen

引用次数: 1

Correction to: Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Undersampled Data in Magnetic Resonance Fingerprinting (MRF) 对磁共振指纹(MRF)中高度欠采样数据的快速和空间受限组织定量的深度学习的修正

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2020-01-01 DOI: 10.1007/978-3-030-00919-9_47

Zhenghan Fang, Yong Chen, Mingxia Liu, Y. Zhan, W. Lin, D. Shen

引用次数: 0

Confounder-Aware Visualization of ConvNets. 可感知混杂因素的 ConvNets 可视化。

Machine learning in medical imaging. MLMI (Workshop) Pub Date : 2019-10-01 Epub Date: 2019-10-10 DOI: 10.1007/978-3-030-32692-0_38

Qingyu Zhao, Ehsan Adeli, Adolf Pfefferbaum, Edith V Sullivan, Kilian M Pohl

{"title":"Confounder-Aware Visualization of ConvNets.","authors":"Qingyu Zhao, Ehsan Adeli, Adolf Pfefferbaum, Edith V Sullivan, Kilian M Pohl","doi":"10.1007/978-3-030-32692-0_38","DOIUrl":"10.1007/978-3-030-32692-0_38","url":null,"abstract":"With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images. To gain a better understanding of how a disease impacts the brain, the studies visualize the salience maps of the ConvNet highlighting voxels within the brain majorly contributing to the prediction. However, these salience maps are generally confounded, i.e., some salient regions are more predictive of confounding variables (such as age) than the diagnosis. To avoid such misinterpretation, we propose in this paper an approach that aims to visualize confounder-free saliency maps that only highlight voxels predictive of the diagnosis. The approach incorporates univariate statistical tests to identify confounding effects within the intermediate features learned by ConvNet. The influence from the subset of confounded features is then removed by a novel partial back-propagation procedure. We use this two-step approach to visualize confounder-free saliency maps extracted from synthetic and two real datasets. These experiments reveal the potential of our visualization in producing unbiased model-interpretation.","PeriodicalId":74092,"journal":{"name":"Machine learning in medical imaging. MLMI (Workshop)","volume":" ","pages":"328-336"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297409/pdf/nihms-1598703.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38058419","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