An anatomically-informed 3D CNN for brain aneurysm classification with weak labels
Lausanne University Hospital and University of Lausanne（洛桑大学医院和洛桑大学）；Center for Biomedical Imaging（生物医学成像中心）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Tommaso Di Noto, Guillaume Marie, Sébastien Tourbier, Yasser Alemán-Gómez, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi
备注：
11 pages, 2 figures, Paper accepted at the "Machine Learning in Clinical Neuroimaging" (MLCN) MICCAI workshop 2020 (https://mlcnws.com/)
A commonly adopted approach to carry out detection tasks in medical imaging is to rely on an initial segmentation. However, this approach strongly depends on voxel-wise annotations which are repetitive and time-consuming to draw for medical experts. An interesting alternative to voxel-wise masks are so-called "weak" labels: these can either be coarse or oversized annotations that are less precise, but noticeably faster to create. In this work, we address the task of brain aneurysm detection as a patch-wise binary classification with weak labels, in contrast to related studies that rather use supervised segmentation methods and voxel-wise delineations. Our approach comes with the non-trivial challenge of the data set creation: as for most focal diseases, anomalous patches (with aneurysm) are outnumbered by those showing no anomaly, and the two classes usually have different spatial distributions. To tackle this frequent scenario of inherently imbalanced, spatially skewed data sets, we propose a novel, anatomically-driven approach by using a multi-scale and multi-input 3D Convolutional Neural Network (CNN). We apply our model to 214 subjects (83 patients, 131 controls) who underwent Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) and presented a total of 111 unruptured cerebral aneurysms. We compare two strategies for negative patch sampling that have an increasing level of difficulty for the network and we show how this choice can strongly affect the results. To assess whether the added spatial information helps improving performances, we compare our anatomically-informed CNN with a baseline, spatially-agnostic CNN. When considering the more realistic and challenging scenario including vessel-like negative patches, the former model attains the highest classification results (accuracy$\simeq$95\%, AUROC$\simeq$0.95, AUPR$\simeq$0.71), thus outperforming the baseline.
Harvard Medical School（哈佛医学院）；The University of Sydney（悉尼大学）；The University of New South Wales（新南威尔士大学）
The structure and variability of the brain's connections can be investigated via prediction of non-imaging phenotypes using neural networks. However, known neuroanatomical relationships between input features are generally ignored in network design. We propose TractGraphCNN, a novel, anatomically informed graph CNN framework for machine learning tasks using diffusion MRI tractography. An EdgeConv module aggregates features from anatomically similar white matter connections indicated by graph edges, and an attention module enables interpretation of predictive white matter tracts. Results in a s…
Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）
Visualizing disease-induced scarring and fibrosis in the heart on cardiac magnetic resonance (CMR) imaging with contrast enhancement (LGE) is paramount in characterizing disease progression and quantifying pathophysiological substrates of arrhythmias. However, segmentation and scar/fibrosis identification from LGE-CMR is an intensive manual process prone to large inter-observer variability. Here, we present a novel fully-automated anatomically-informed deep learning solution for left ventricle (LV) and scar/fibrosis segmentation and clinical feature extraction from LGE-CMR. The technology invo…
TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography
Brigham and Women's Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）；University of Electronic Science and Technology of China（电子科技大学）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；The University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Boston University School of Medicine（波士顿大学医学院）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）
REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis
Department of Computer Science, Northwestern University McCormick School of Engineering and Applied Science（西北大学麦考密克工程与应用科学学院计算机科学系）；Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院肺与危重症医学科）；Division of Rheumatology, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院风湿病科）；Simpson Querrey Lung Institute for Translational Science, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院辛普森·奎雷转化科学肺研究所）；Department of Electrical and Computer Engineering, Northwestern University McCormick School of Engineering and Applied Science（西北大学麦考密克工程与应用科学学院电气与计算机工程系）；Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University Feinberg School of Medicine（西北大学范伯格医学院放射科机器与混合智能实验室）
Existing Bayesian spatial priors for functional magnetic resonance imaging (fMRI) data correspond to stationary isotropic smoothing filters that may oversmooth at anatomical boundaries. We propose two anatomically informed Bayesian spatial models for fMRI data with local smoothing in each voxel based on a tensor field estimated from a T1-weighted anatomical image. We show that our anatomically informed Bayesian spatial models results in posterior probability maps that follow the anatomical structure.
Bruno Kessler Foundation（布鲁诺·凯斯勒基金会）；University of Trento（特伦托大学）；Indiana University（印第安纳大学）
Segmenting white matter bundles from human tractograms is a task of interest for several applications. Current methods for bundle segmentation consider either only prior knowledge about the relative anatomical position of a bundle, or only its geometrical properties. Our aim is to improve the results of segmentation by proposing a method that takes into account information about both the underlying anatomy and the geometry of bundles at the same time. To achieve this goal, we extend a state-of-the-art example-based method based on the Linear Assignment Problem (LAP) by including prior anatomic…
BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet
University of Pittsburgh（匹兹堡大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cedars-Sinai Medical Center（西德萨斯医疗中心）；Mayo Clinic Arizona（梅奥诊所亚利桑那分部）
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Yale University（耶鲁大学）；IBM Research（IBM研究院）；Yale New Haven Hospital（耶鲁-纽黑文医院）；Yale School of Medicine（耶鲁医学院）
SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃森哲-埃尔朗根-纽伦堡大学模式识别实验室）；Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根大学医院放射学研究所）；Institut für Informationsverarbeitung, Leibniz Universität Hannover（汉诺威莱比锡大学信息处理研究所）；Department of Radiology, Harvard Medical School and Massachusetts General Hospital（哈佛医学院放射科和麻省总医院）
The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help mitigate such training obstacles, most data augmentation methods have been developed for natural images as in computer vision tasks such as CIFAR, not for medical images. This work helps to fills in this gap by proposing a method for generating new functional Magnetic Resonance Images (fMRI) with realistic brain morphology. This method is tested on a challenging task of predicting antidepressant treatment response f…
Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Washing University in St. Louis（圣路易斯华盛顿大学）；First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）
ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（生物医学工程学院，生命科学与医学系，中国科学技术大学）；Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE) Lab, YRD-RIGHT, USTC Suzhou Institute for Advanced Research（医学影像、机器人、分析计算与学习（MIRACLE）实验室，YRD-RIGHT，中国科学技术大学苏州研究院）；Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology（江苏省多模态数字孪生技术重点实验室）；Biomedical Basic Research Center (BBRC) of Jiangsu Province（江苏省生物医学基础研究中心）；Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, USTC（放射科，中国科学技术大学第一附属医院，生命科学与医学系，中国科学技术大学）；Anhui IFLYTEK CO., Ltd（安徽科大讯飞股份有限公司）；School of Medicine, Stanford University（医学院，斯坦福大学）；State Key Laboratory of Precision and Intelligent Chemistry, Hefei, Anhui, China（安徽省精密与智能化学重点实验室，合肥，安徽，中国）
A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces
Hawkes Institute, University College London, UK Unit for Lifelong Health；Aging, University College London, UK Bioengineering Department；Imperial-X, Imperial College London, UK Department of Diagnostic Radiology, Copenhagen University Hospital, Denmark Department of Radiology \& Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, The Netherlands Department of Radiology；Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands Queen Square Institute of Neurology, University College London, UK Institute of Cardiovascular Sciences, University College London, UK Barts Heart Centre, St Bartholomew's Hospital, London, UK
