Information processing in medical imaging : proceedings of the ... conference最新文献

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation 联邦三维医学体分割的邻域特征统计增强

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-10-23 DOI: 10.1007/978-3-031-34048-2_28

Y. Huang, Wanqing Xie, Mingzhen Li, Mingmei Cheng, Jinzhou Wu, Weixiao Wang, Jane You, Xiaofeng Liu

引用次数: 1

Better Generalization of White Matter Tract Segmentation to Arbitrary Datasets with Scaled Residual Bootstrap 基于残差自举的白质束分割在任意数据集上的更好泛化

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-09-25 DOI: 10.1007/978-3-031-34048-2_48

Wan Liu, Chuyang Ye

引用次数: 0

Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision 基于粗到精自监督的息肉分割模型无监督自适应

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-08-13 DOI: 10.1007/978-3-031-34048-2_20

Jiexiang Wang, Chaoqi Chen

引用次数: 0

Weakly Semi-supervised Detection in Lung Ultrasound Videos 肺超声视频的弱半监督检测

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-08-08 DOI: 10.1007/978-3-031-34048-2_16

J. Ouyang, Li Chen, Gary Y. Li, Naveen Balaraju, Shubham Patil, C. Mehanian, Sourabh Kulhare, R. Millin, K. Gregory, Cynthia Gregory, Meihua Zhu, David O. Kessler, L. Malia, Almaz S. Dessie, J. Rabiner, D. Coneybeare, B. Shopsin, A. Hersh, C. Madar, J. Shupp, L. Johnson, Jacob Avila, K. Dwyer, P. Weimersheimer, B. Raju, J. Kruecker, A. Chen

引用次数: 2

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds. mSPD-NN：用于从功能连接组学簇中发现生物标记物的几何感知神经框架。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08

Niharika S D'Souza, Archana Venkataraman

{"title":"mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.","authors":"Niharika S D'Souza, Archana Venkataraman","doi":"","DOIUrl":"","url":null,"abstract":"Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks ignore the underlying data geometry. This is largely because simple operations, such as mean estimation, do not have easily computable closed-form solutions. We propose a geometrically aware neural framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic mean of a collections of symmetric positive definite (SPD) matrices. The mSPD-NN is comprised of bilinear fully connected layers with tied weights and utilizes a novel loss function to optimize the matrix-normal equation arising from Fréchet mean estimation. Via experiments on synthetic data, we demonstrate the efficacy of our mSPD-NN against common alternatives for SPD mean estimation, providing competitive performance in terms of scalability and robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in multiple experiments on rs-fMRI data and demonstrate that it uncovers stable biomarkers associated with subtle network differences among patients with ADHD-ASD comorbidities and healthy controls.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"2023 ","pages":"53-65"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11513587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523786","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

Hierarchical Geodesic Polynomial Model for Multilevel Analysis of Longitudinal Shape. 纵向形状多层次分析的分层测地线多项式模型。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 DOI: 10.1007/978-3-031-34048-2_62

Ye Han, Jared Vicory, Guido Gerig, Patricia Sabin, Hannah Dewey, Silvani Amin, Ana Sulentic, Christian Hertz, Matthew Jolley, Beatriz Paniagua, James Fishbaugh

{"title":"Hierarchical Geodesic Polynomial Model for Multilevel Analysis of Longitudinal Shape.","authors":"Ye Han, Jared Vicory, Guido Gerig, Patricia Sabin, Hannah Dewey, Silvani Amin, Ana Sulentic, Christian Hertz, Matthew Jolley, Beatriz Paniagua, James Fishbaugh","doi":"10.1007/978-3-031-34048-2_62","DOIUrl":"https://doi.org/10.1007/978-3-031-34048-2_62","url":null,"abstract":"Longitudinal analysis is a core aspect of many medical applications for understanding the relationship between an anatomical subject's function and its trajectory of shape change over time. Whereas mixed-effects (or hierarchical) modeling is the statistical method of choice for analysis of longitudinal data, we here propose its extension as hierarchical geodesic polynomial model (HGPM) for multilevel analyses of longitudinal shape data. 3D shapes are transformed to a non-Euclidean shape space for regression analysis using geodesics on a high dimensional Riemannian manifold. At the subject-wise level, each individual trajectory of shape change is represented by a univariate geodesic polynomial model on timestamps. At the population level, multivariate polynomial expansion is applied to uni/multivariate geodesic polynomial models for both anchor points and tangent vectors. As such, the trajectory of an individual subject's shape changes over time can be modeled accurately with a reduced number of parameters, and population-level effects from multiple covariates on trajectories can be well captured. The implemented HGPM is validated on synthetic examples of points on a unit 3D sphere. Further tests on clinical 4D right ventricular data show that HGPM is capable of capturing observable effects on shapes attributed to changes in covariates, which are consistent with qualitative clinical evaluations. HGPM demonstrates its effectiveness in modeling shape changes at both subject-wise and population levels, which is promising for future studies of the relationship between shape changes over time and the level of dysfunction severity on anatomical objects associated with disease.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"810-821"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10323213/pdf/nihms-1912654.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9807388","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

Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. 基于生成式深度网络的模式生物概率分段刚性地图集学习。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 DOI: 10.1007/978-3-031-34048-2_26

Amin Nejatbakhsh, Neel Dey, Vivek Venkatachalam, Eviatar Yemini, Liam Paninski, Erdem Varol

{"title":"Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks.","authors":"Amin Nejatbakhsh, Neel Dey, Vivek Venkatachalam, Eviatar Yemini, Liam Paninski, Erdem Varol","doi":"10.1007/978-3-031-34048-2_26","DOIUrl":"https://doi.org/10.1007/978-3-031-34048-2_26","url":null,"abstract":"Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"332-343"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358289/pdf/nihms-1910173.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10239564","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

TetCNN: Convolutional Neural Networks on Tetrahedral Meshes. TetCNN：四面体网格上的卷积神经网络

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08 DOI: 10.1007/978-3-031-34048-2_24

Mohammad Farazi, Zhangsihao Yang, Wenhui Zhu, Peijie Qiu, Yalin Wang

{"title":"TetCNN: Convolutional Neural Networks on Tetrahedral Meshes.","authors":"Mohammad Farazi, Zhangsihao Yang, Wenhui Zhu, Peijie Qiu, Yalin Wang","doi":"10.1007/978-3-031-34048-2_24","DOIUrl":"10.1007/978-3-031-34048-2_24","url":null,"abstract":"Convolutional neural networks (CNN) have been broadly studied on images, videos, graphs, and triangular meshes. However, it has seldom been studied on tetrahedral meshes. Given the merits of using volumetric meshes in applications like brain image analysis, we introduce a novel interpretable graph CNN framework for the tetrahedral mesh structure. Inspired by ChebyNet, our model exploits the volumetric Laplace-Beltrami Operator (LBO) to define filters over commonly used graph Laplacian which lacks the Riemannian metric information of 3D manifolds. For pooling adaptation, we introduce new objective functions for localized minimum cuts in the Graclus algorithm based on the LBO. We employ a piece-wise constant approximation scheme that uses the clustering assignment matrix to estimate the LBO on sampled meshes after each pooling. Finally, adapting the Gradient-weighted Class Activation Mapping algorithm for tetrahedral meshes, we use the obtained heatmaps to visualize discovered regions-of-interest as biomarkers. We demonstrate the effectiveness of our model on cortical tetrahedral meshes from patients with Alzheimer's disease, as there is scientific evidence showing the correlation of cortical thickness to neurodegenerative disease progression. Our results show the superiority of our LBO-based convolution layer and adapted pooling over the conventionally used unitary cortical thickness, graph Laplacian, and point cloud representation.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"303-315"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10765307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139099273","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

MeshDeform: Surface Reconstruction of Subcortical Structures in Human Brain MRI. 网格变形：人脑MRI皮层下结构的表面重建。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08 DOI: 10.1007/978-3-031-34048-2_41

Junjie Zhao, Siyuan Liu, Sahar Ahmad, Yap Pew-Thian

{"title":"MeshDeform: Surface Reconstruction of Subcortical Structures in Human Brain MRI.","authors":"Junjie Zhao, Siyuan Liu, Sahar Ahmad, Yap Pew-Thian","doi":"10.1007/978-3-031-34048-2_41","DOIUrl":"10.1007/978-3-031-34048-2_41","url":null,"abstract":"Surface reconstruction of cortical and subcortical structures is crucial for brain morphological studies. Existing deep learning surface reconstruction methods, such as DeepCSR and Vox2Surf, learn an implicit field function for computing the isosurface, but do not consider mesh topology. In this paper, we propose a novel and efficient deep learning mesh deformation network, called MeshDeform, to reconstruct topologically correct surfaces of subcortical structures using brain MR images. MeshDeform combines features extracted from a U-Net encoder with mesh deformation blocks to predict surfaces of subcortical structures by deforming spherical mesh templates. MeshDeform is able to reconstruct in less than 10 seconds the surfaces of a left-right pair of subcortical structures with subvoxel accuracy. Reconstruction of all 17 subcortical structures takes less than one and a half minutes. By contrast, Vox2Surf takes about 20-30 minutes for all subcortical structures. Visual and quantitative evaluation on the Human Connectome Project (HCP) dataset demonstrate that MeshDeform generates accurate subcortical surfaces in limited time while preserving mesh topology.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"536-547"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71429784","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

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-Aware Contrastive Distillation. 利用解剖感知对比蒸馏技术引导半监督医学图像分割。

Information processing in medical imaging : proceedings of the ... conference Pub Date : 2023-06-01 Epub Date: 2023-06-08 DOI: 10.1007/978-3-031-34048-2_49

Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S Duncan

{"title":"Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-Aware Contrastive Distillation.","authors":"Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S Duncan","doi":"10.1007/978-3-031-34048-2_49","DOIUrl":"10.1007/978-3-031-34048-2_49","url":null,"abstract":"Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation. Specifically, we first develop an iterative contrastive distillation algorithm by softly labeling the negatives rather than binary supervision between positive and negative pairs. We also capture more semantically similar features from the randomly chosen negative set compared to the positives to enforce the diversity of the sampled data. Second, we raise a more important question: Can we really handle imbalanced samples to yield better performance? Hence, the key innovation in ACTION is to learn global semantic relationship across the entire dataset and local anatomical features among the neighbouring pixels with minimal additional memory footprint. During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation boundaries and more accurate predictions. Extensive experiments across two benchmark datasets and different unlabeled settings show that ACTION significantly outperforms the current state-of-the-art semi-supervised methods.","PeriodicalId":73379,"journal":{"name":"Information processing in medical imaging : proceedings of the ... conference","volume":"13939 ","pages":"641-653"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322187/pdf/nihms-1913000.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9794875","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