Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so image volumes are degraded by inter- and intra- slice motion artifacts. Besides, manual annotation for fetal MR image quality assessment are usually time-consuming. Therefore, in this work, a semi-supervised deep learning method that detects slices with artifacts during the brain volume scan is proposed. Our method is based on the mean teacher model, where we not only enforce consistency between student and teacher models on the whole image, but also adopt an ROI consistency loss to guide the network to focus on the brain region. The proposed method is evaluated on a fetal brain MR dataset with 11,223 labeled images and more than 200,000 unlabeled images. Results show that compared with supervised learning, the proposed method can improve model accuracy by about 6\% and outperform other state-of-the-art semi-supervised learning methods. The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.
MIT（麻省理工学院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and dynamics of fetal function, is limited to fast imaging techniques with compromises in image quality and resolution. Super-resolution for dynamic fetal MRI is still a challenge, especially when multi-oriented stacks of image slices for oversampling are not available and high temporal resolution for recording the dynamics of the fetus or placenta is desired. Further, fetal motion makes it difficult to acquire high-resolution images for supervi…
Zhejiang University（浙江大学）；Shandong Medical Imaging Research Institute（山东医学影像学研究所）；Cheeloo College of Medicine, Shandong University（山东大学齐鲁医学院）；Shanghai Jiao Tong University（上海交通大学）
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequent…
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
CIBM -- Center for Biomedical Imaging（CIBM生物医学成像中心）；Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne（诊断与介入放射学系，洛桑大学医院和洛桑大学）；Aix-Marseille Université（阿维尼昂-马赛大学）；BCN MedTech（BCN医疗技术）；BCNatal Fetal Medicine Research Center（BCNatal胎儿医学研究中心）；IDIBAPS and CIBERER（IDIBAPS与CIBERER）；Center for MR Research（磁共振研究中心）；Neuroscience Center Zurich（苏黎世神经科学中心）；Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD)（发展与学习适应性脑回路研究优先项目）
Robustness and Diagnostic Performance of Super-Resolution Fetal Brain MRI
Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia（卢布尔雅那大学医学学院）；University Medical Centre Ljubljana, Ljubljana, Slovenia（卢布尔雅那大学医学中心）；Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia（卢布尔雅那大学电气工程学院）
Universitair Ziekenhuis Leuven（鲁汶大学医院）；Medical University of Vienna（维也纳医科大学）；King’s College London（伦敦国王学院）
In fetal Magnetic Resonance Imaging, Super Resolution Reconstruction (SRR) algorithms are becoming popular tools to obtain high-resolution 3D volume reconstructions from low-resolution stacks of 2D slices, acquired at different orientations. To be effective, these algorithms often require accurate segmentation of the region of interest, such as the fetal brain in suspected pathological cases. In the case of Spina Bifida, Ebner, Wang et al. (NeuroImage, 2020) combined their SRR algorithm with a 2-step segmentation pipeline (2D localisation followed by a 2D segmentation network). However, if the…
Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations
School of Computation, Information and Technology, and School of Medicine and Health, Technical University Munich（计算、信息与技术学院，以及医学与健康学院，技术大学慕尼黑）；CIBM Center for Biomedical Imaging and Radiology Department of Lausanne University Hospital and University of Lausanne（日内瓦大学医院与日内瓦大学生物医学影像与放射学中心部门）；Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London（发展脑中心，生物医学工程与影像科学学院，伦敦国王学院）；Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid（生物医学影像技术，电信工程学院，马德里理工大学）；Department of Computing, Imperial College London（计算学院，伦敦帝国学院）
3D Magnetic Resonance Imaging (MRI) is often a trade-off between fast but low-resolution image acquisition and highly detailed but slow image acquisition. Fast imaging is required for targets that move to avoid motion artefacts. This is in particular difficult for fetal MRI. Spatially independent upsampling techniques, which are the state-of-the-art to address this problem, are error prone and disregard contextual information. In this paper we propose a context-sensitive upsampling method based on a residual convolutional neural network model that learns organ specific appearance and adopts se…
Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping
Department of Radiology, Lausanne University Hospital（放射科部门，洛桑大学医院）；University of Lausanne（洛桑大学）；CIBM Center for Biomedical Imaging（生物医学成像中心）；Chair for AI in Healthcare and Medicine（医疗与健康人工智能主席职位）；Technical University of Munich (TUM)（慕尼黑技术大学）；TUM University Hospital（慕尼黑技术大学医院）；Department of Computing, Imperial College London（计算部门，伦敦帝国理工学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Biomedical Engineering Department（生物医学工程部门）；School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）；Institut de Neurosciences de la Timone（蒂莫尼神经科学研究所）；IMT Atlantique（大西洋研究所）；Smart Imaging Lab, Friedrich-Alexander University Erlangen（智能成像实验室，埃朗根-纽伦堡大学）
MIT（麻省理工学院）；Tsinghua University（清华大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI ob…
MIT（麻省理工学院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making. Current developments of deep reinforcement learning (DRL), offer a nov…
Northeastern University（东北大学）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Fetal brain segmentation is an important first step for slice-level motion correction and slice-to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering. To address this critical unmet need, in this work we analyzed the speed-accuracy performance of a variety of deep neural network models, and devised a symbolically small convolutional neural network that combines spatial details at high resolution with context features extracted at …
Enhancing Generalized Fetal Brain MRI Segmentation using A Cascade Network with Depth-wise Separable Convolution and Attention Mechanism
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence（教育部计算神经科学与类脑智能重点实验室）；MOE Frontiers Center for Brain Science（教育部脑科学前沿中心）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）；University of Ottawa（渥太华大学）；Children's Hospital of Eastern Ontario（东安大略儿童医院）；Shaare Zedek Medical Center（沙雷泽德克医疗中心）
Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements…
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Technical University of Munich（慕尼黑工业大学）
Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone to motion artifacts that can affect the correctness of both manual and automatic segmentation techniques. In this paper, we propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation, called CAS-Net. The condition…
DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI
King’s College London（伦敦国王学院）；Evelina London Children’s Hospital（伊芙琳娜伦敦儿童医院）；Imperial College London（帝国理工学院）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；Tel Aviv University（特拉维夫大学）
Volumetric measurements of fetal structures in MRI are time consuming and error prone and therefore require automatic segmentation. Placenta segmentation and accurate fetal brain segmentation for gyrification assessment are particularly challenging because of the placenta fuzzy boundaries and the fetal brain cortex complex foldings. In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss. The loss is computed efficiently for each slice via erosion, dilation and XOR operators. We describe …
MIT（麻省理工学院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；University College London（伦敦大学学院）；Massachusetts General Hospital（麻省总医院）
Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from othe…
University of Electronic Science and Technology of China（电子科技大学）；King’s College London（伦敦国王学院）；University Hospitals KU Leuven（鲁汶大学附属医院）；University College London（伦敦大学学院）；SenseTime Research（商汤科技研究院）
Segmentation of the fetal brain from stacks of motion-corrupted fetal MRI slices is important for motion correction and high-resolution volume reconstruction. Although Convolutional Neural Networks (CNNs) have been widely used for automatic segmentation of the fetal brain, their results may still benefit from interactive refinement for challenging slices. To improve the efficiency of interactive refinement process, we propose an Uncertainty-Guided Interactive Refinement (UGIR) framework. We first propose a grouped convolution-based CNN to obtain multiple automatic segmentation predictions with…
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Lausanne University Hospital（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；King’s College London（伦敦国王学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
University Medical Center Utrecht（乌得勒支大学医学中心）；Wilhelmina Children’s Hospital（威廉明娜儿童医院）；Utrecht University（乌得勒支大学）
MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias f…
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning
King’s College London（伦敦国王学院）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Guy’s & St. Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；Evelina London Children’s Hospital（伊夫琳娜伦敦儿童医院）
In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR…
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automatic segmentation of the cortical plate, on the other hand, is challenged by the relatively low resolution of the reconstructed fetal brain MRI scans compared to the thin structure of the cortical plate, partial voluming, and the wide range of variations in the morphology of the cortical plate as the brain matures during gestation. To reduce the burde…
Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features from multi-contrast MRI for multi-fetal organ analysis of FGR. We employed T2 relaxometry and diffusion-weighted MRI datasets (using a combined T2-diffusion scan) for 12 normally grown and 12 FGR gestational age (GA) matched pregnancies. We applied the Intravoxel Incoherent Motion M…
Segmentation of the developing fetal brain is an important step in quantitative analyses. However, manual segmentation is a very time-consuming task which is prone to error and must be completed by highly specialized indi-viduals. Super-resolution reconstruction of fetal MRI has become standard for processing such data as it improves image quality and resolution. However, dif-ferent pipelines result in slightly different outputs, further complicating the gen-eralization of segmentation methods aiming to segment super-resolution data. Therefore, we propose using transfer learning with noisy mul…
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for anomaly screening. For this ultrasound (US) is employed. While expert sonographers are adept at reading US images, MR images are much easier for non-experts to interpret. Hence in this paper we seek to produce images with MRI-like appearance directly from clinical US images. Our own clinical motivation is to seek a way to communicate US findings to patients or clinical professionals unfamiliar with US, but in medical image analysis such a capability is potentially useful, for in…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
organization= Department of Radiology, Lausanne University Hospital；University of Lausanne , city= Lausanne , country= Switzerland；organization= CIBM Center for Biomedical Imaging , city= Lausanne , country= Switzerland；organization= Department of Early Life Imaging, School of Biomedical Engineering \& Imaging Sciences, King’s College London , city= London , country= UK；organization= Smart Imaging Lab, University Hospital Erlangen , city= Erlangen , country= Germany；organization= Center for MR-Research, University Children’s Hospital Zurich, University of Zurich , city= Zurich , country= Switzerland；organization= Neuroscience Center Zurich, University of Zurich , city= Zurich , country= Switzerland；organization= National Heart \& Lung Institute, Imperial College London , city= London , country= UK；organization= University of California, San Francisco；UCSF Benioff Children’s Hospital , city= San Francisco , state= California , country= USA；organization= Department of Quantitative Biomedicine, University of Zurich , city= Zurich , country= Switzerland；organization= Department of Informatics, Technical University of Munich , city= Munich , country= Germany；organization= Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；organization= Neuroimaging Unit, Scientific Institute IRCCS E. Medea , city= Bosisio Parini , country= Italy；organization= Department of Informatics, Systems；Communication, University of Milano Bicocca , city= Milan , country= Italy；organization= Research Institute of Computer Vision；organization= BCN MedTech, Department of Engineering, Universitat Pompeu Fabra , city= Barcelona , country= Spain；organization= Department of Information Engineering, University of Padova , city= Padova , country= Italy；organization= Institut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision；organization= Inria, HeKA, PariSantéCampus , city= Paris , country= France；organization= L. D. College of Engineering , city= Gujarat , country= India；organization= Medical Faculty Heidelberg, Heidelberg University , addressline= Pattern Analysis；Learning Group, Department of Radiation Oncology, Heidelberg University Hospital , city= Heidelberg , country= Germany；organization= Canon Medical Systems (China) Co., Ltd , city= , country= China；organization= Faculty of Electrical Engineering, University of Ljubljana , city= Ljubljana , country= Slovenia；organization= Department of Radiology, Seoul National University Hospital , city= Seoul , country= South Korea；organization= School of Mechanical；Electrical Engineering, University of Electronic Science；organization= School of Computer Science, Wuhan University , city= Wuhan , country= China；Developmental Science Center, Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；organization= Hawkes Institute, Department of Computer Science, University College London , city= London , country= UK；organization= Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School , city= Charlestown , state= Massachusetts , country= USA；organization= Department of Biomedical Imaging；Image-guided Therapy, Computational Imaging Research Lab (CIR), Early Life Image Analysis Group, Medical University of Vienna , city= Vienna , country= Austria；organization= University Research Priority Project Adaptive Brain Circuits in Development；Learning (AdaBD), University of Zurich , city= Zurich , country= Switzerland；organization= Sagol Brain Institute, Tel Aviv Sourasky Medical Center；School of EE, Tel-Aviv University , city= Tel-Aviv , country= Israel；organization= Department of Medical Imaging Sciences, The Faculty of Social Welfare；Health Sciences, University of Haifa , city= Haifa , country= Israel；Faculty of Medicine；Sagol School of Neuroscience, Tel-Aviv University , city= Tel-Aviv , country= Israel；organization= Department Woman-Mother-Child, CHUV , city= Lausanne , country= Switzerland；organization= BCNatal Fetal Medicine Research Center (Hospital Clínic；Hospital Sant Joan de Déu), Universitat de Barcelona , city= Barcelona , country= Spain；organization= German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing , city= Heidelberg , country= Germany；organization= Helmholtz Imaging, German Cancer Research Center (DKFZ) , city= Heidelberg , country= Germany；organization= Faculty of Mathematics；Computer Science, Heidelberg University , city= Heidelberg , country= Germany；organization= University of Zurich , city= Zurich , country= Switzerland；organization= Croatian Institute for Brain Research, School of Medicine, University of Zagreb , city= Zagreb , country= Croatia；organization= Department of Biomedical Engineering, School of Biomedical Engineering \& Imaging Sciences, King’s College , city= London , country= United Kingdom；Musculoskeletal Radiology, Medical University of Vienna , city= Vienna , country= Austria；organization= Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA；organization= Department of Radiology, Boston Children’s Hospital, Harvard Medical School , city= Boston , state= Massachusetts , country= USA
Open spina bifida (SB) is one of the most common congenital defects and can lead to impaired brain development. Emerging fetal surgery methods have shown considerable success in the treatment of patients with this severe anomaly. Afterwards, alterations in the brain development of these fetuses have been observed. Currently no longitudinal studies exist to show the effect of fetal surgery on brain development. In this work, we present a fetal MRI neuro-imaging analysis pipeline for fetuses with SB, including automated fetal ventricle segmentation and deformation-based morphometry, and demonstr…
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Hyperfine Research Inc.（Hyperfine研究公司）
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help b…
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this paper we propose to generate MR-like images directly from clinical US images. In medical image analysis such a capability is potentially useful as well, for instance for automatic US-MRI registration and fusion. The proposed model is end…
Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentation
Department of Radiology, Lausanne University Hospital and University of Lausanne (UNIL)（拉沃斯大学医院放射科和洛桑大学（UNIL））；CIBM Center for Biomedical Imaging（生物医学成像中心）；Institute for Information Processing, Leibniz University Hannover（汉诺威莱比锡大学信息处理研究所）；Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程系）；Department of Biomedical Imaging and Image-Guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna（维也纳医学大学生物医学成像与影像引导治疗系）；Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna（维也纳医学大学生物医学成像与影像引导治疗系，计算成像研究实验室（CIR））；Christian Doppler Laboratory for Mathematical Modelling and Simulation of Next-Generation Medical Ultrasound Devices, Medical University of Vienna（维也纳医学大学下一代医学超声设备数学建模与仿真克里斯蒂安多普勒实验室）；Comprehensive Center for Artificial Intelligence in Medicine, Medical University of Vienna（维也纳医学大学人工智能在医学中的综合中心）；Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image–guided Therapy, Medical University of Vienna（维也纳医学大学生物医学成像与影像引导治疗系，神经放射学和骨科放射学系）
AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI
College of Biomedical Engineering & Instrument Science, Zhejiang University（浙江大学生物医学工程与仪器科学学院）；Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences（中国医学科学院北京医院国家老年医学中心老年医学研究所）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）；State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）
The fetal cortical plate (CP) undergoes drastic morphological changes during the in utero development. Therefore, CP growth and folding patterns are key indicator in the assessment of the brain development and maturation. Magnetic resonance imaging (MRI) offers specific insights for the analysis of quantitative imaging biomarkers. Nonetheless, accurate and, more importantly, topologically correct MR image segmentation remains the key baseline to such analysis. In this study, we propose a deep learning segmentation framework for automatic and morphologically consistent segmentation of the CP in…
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
Harvard Medical School（哈佛医学院）；Shenzhen University（深圳大学）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）
University Hospitals Leuven（鲁汶大学医院）；University Hospitals Tanta（坦塔大学医院）；University College London（伦敦大学学院）
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fu…
University College London（伦敦大学学院）；University of Oxford（牛津大学）；Siemens Healthineers（西门子医疗）；King’s College London（伦敦国王学院）
Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the same paradigm and typically predict, for any new input image pair, dense correspondences in the form of a dense displacement field or parameters of a spatial transformation model. However, in many applications of registration, the spatial transformation itself is only required to propagate points or regions of interest (ROIs). In such cases, detaile…
Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg
University Children’s Hospital Zurich（苏黎世大学儿童医院）；ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；Medical University of Vienna（维也纳医科大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；King's College London（伦敦国王学院）；University Hospital Erlangen（埃尔朗根大学医院）；Spanish National Research Council (CSIC)（西班牙国家研究委员会）；Universitat Politècnica de València（瓦伦西亚理工大学）
Universidad Politécnica de Madrid（马德里理工大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络中心（生物工程、生物材料与纳米医学））；King’s College London（伦敦国王学院）
Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructi…
Detailed delineation of the fetal brain in diffusion MRI via multi-task learning
Harvard Medical School（哈佛医学院）；Elmhurst Hospital Center（埃尔姆赫斯特医院中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Massachusetts General Hospital（麻省总医院）；University of California Irvine（加州大学欧文分校）
FetMRQC: a robust quality control system for multi-centric fetal brain MRI
CIBM – Center for Biomedical Imaging（生物医学成像中心（CIBM））；BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu)（BCNatal胎儿医学研究中心（克利尼克医院与圣茹安德迪乌医院））；Universitat de Barcelona（巴塞罗那大学）；Aix-Marseille Université（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；Institut de Neurosciences de La Timone（拉蒂讷神经科学研究所）；Hôpital Timone, AP-HM（Assistance Publique – Hôpitaux de Marseille 旗下的蒂莫讷医院）；University Children’s Hospital Zurich, University of Zurich（苏黎世大学儿童医院、苏黎世大学）；Neuroscience Center Zurich, University of Zurich（苏黎世大学神经科学中心）
Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Radiology, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院放射科）
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. …
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
University Hospitals Leuven（鲁汶大学医院）；Technical University Munich（慕尼黑工业大学）；University Hospital Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；University College London（伦敦大学学院）；University Hospitals Tanta（坦塔大学医院）；Medical University of Vienna（维也纳医科大学）
FetMRQC: Automated Quality Control for fetal brain MRI
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)（洛桑大学医院（CHUV）和洛桑大学（UNIL）放射科）；CIBM Center for Biomedical Imaging（CIBM生物医学影像中心）；BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona（巴塞罗那大学BCNatal胎儿医学研究中心（Clínic医院和Sant Joan de Déu医院））；Department Woman-Mother-Child, CHUV（CHUV妇女-母亲-儿童科）；IDIBAPS and CIBERER（IDIBAPS和CIBERER）
Deformable image registration is able to achieve fast and accurate alignment between a pair of images and thus plays an important role in many medical image studies. The current deep learning (DL)-based image registration approaches directly learn the spatial transformation from one image to another by leveraging a convolutional neural network, requiring ground truth or similarity metric. Nevertheless, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within images. Moreover, DL-…
CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）；Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；University of Lausanne (UNIL)（洛桑大学（UNIL））
Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thick slices (volumes). In practice, only a few low-resolution volumes are acquired. Thus, optimization-based image-reconstruction methods require strong regularization using hand-crafted regularizers (e.g., TV). Yet, due to in utero fetal motion and the rapidly changing fetal brain anatomy, the acquisition of the high-resolution images that are required to train supervised learning methods is difficult. In this paper, …
Fetal brain magnetic resonance imaging serves as an emerging modality for prenatal counseling and diagnosis in disorders affecting the brain. Machine learning based segmentation plays an important role in the quantification of brain development. However, a limiting factor is the lack of sufficiently large, labeled training data. Our study explored the application of SPADE, a conditional general adversarial network (cGAN), which learns the mapping from the label to the image space. The input to the network was super-resolution T2-weighted cerebral MRI data of 120 fetuses (gestational age range:…
