Automated fetal brain extraction from full-uterus MRI is a challenging task due to variable head sizes, orientations, complex anatomy, and prevalent artifacts. While deep-learning (DL) models trained on synthetic images have been successful in adult brain extraction, adapting these networks for fetal MRI is difficult due to the sparsity of labeled data, leading to increased false-positive predictions. To address this challenge, we propose a test-time strategy that reduces false positives in networks trained on sparse, synthetic labels. The approach uses a breadth-fine search (BFS) to identify a subvolume likely to contain the fetal brain, followed by a deep-focused sliding window (DFS) search to refine the extraction, pooling predictions to minimize false positives. We train models at different window sizes using synthetic images derived from a small number of fetal brain label maps, augmented with random geometric shapes. Each model is trained on diverse head positions and scales, including cases with partial or no brain tissue. Our framework matches state-of-the-art brain extraction methods on clinical HASTE scans of third-trimester fetuses and exceeds them by up to 5\% in terms of Dice in the second trimester as well as EPI scans across both trimesters. Our results demonstrate the utility of a sliding-window approach and combining predictions from several models trained on synthetic images, for improving brain-extraction accuracy by progressively refining regions of interest and minimizing the risk of missing brain mask slices or misidentifying other tissues as brain.
Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation
Computational Imaging Research Lab（计算成像研究实验室）；Department of Biomedical Imaging and Image-guided Therapy（生物医学成像与影像引导治疗部门）；Medical University of Vienna（维也纳医科大学）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Division of General Radiology（普通放射学分会）；Medical University Vienna（维也纳医科大学）；Christian Doppler Laboratory for Mathematical Modelling and Simulation of Next-Generation Medical Ultrasound Devices（基督教·多普勒医学影像建模与模拟实验室）
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）
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…
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；Technical University of Munich（慕尼黑工业大学）；Brain Research Institute, University of Zurich（苏黎世大学脑研究所）；University of Debrecen（德布勒森大学）
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open databases of segmented fetal brains. Here we introduce a publicly available database of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (extern…
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 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 model…
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…
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）
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…
University of Oxford（牛津大学）；Amazon（亚马逊）；Kings College London（伦敦国王学院）
This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound images to build a biometry solution for low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised domain adaptation approach to train deep models to be invariant to significant image distribution shift between the image types. Our proposed method, which employs a Dual Adversarial Calibration (DAC) framework, consists of adversarial pathways which enforce model invariance to; i) adversarial perturb…
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation
Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；University of Lausanne (UNIL)（洛桑大学（UNIL））；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University Children’s Hospital Zurich（苏黎世大学儿童医院）；University of Zurich（苏黎世大学）；Neuroscience Center Zurich（苏黎世神经科学中心）
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 …
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…
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学工程科学系生物医学工程研究所）；Nuffield Department of Obstetrics and Gynaecology, John Radcliffe Hospital, University of Oxford（牛津大学约翰拉德克利夫医院妇产科）；Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford（牛津大学李兆基健康信息与发现中心大数据研究所）
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, rega…
Wayne State University（韦恩州立大学）；New York University（纽约大学）
Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into development of the human brain before birth, but these studies have predominately focused on brain functional connectivity (i.e. Fisher z-score), which requires manual processing steps for feature extraction from fMRI images. Deep learning approaches (i.e., Convolutional Neural Networks) have achieved remarkable success on learning directly fro…
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…
SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）；Massachusetts Institute of Technology（麻省理工学院）；University of California Irvine（加州大学欧文分校）
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）
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）
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）
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）
Lausanne University Hospital (CHUV)（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）；Siemens Healthcare（西门子医疗）；Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a …
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…
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segm…
Origin Health India（印度Origin Health）；Bangalore Fetal Medicine Center（班加罗尔胎儿医学中心）；Prime Imaging and Prenatal Diagnostics（Prime影像与产前诊断中心）；Kasturba Medical College, Manipal Academy of Higher Education（马尼帕尔高等教育学院卡斯特尔巴医学院）；Origin Health；Universiti Malaya（马来亚大学）；Aarthi Scans and Labs（Aarthi扫描与实验室）
Quality assessment of prenatal ultrasonography is essential for the screening of fetal central nervous system (CNS) anomalies. The interpretation of fetal brain structures is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. With recent advancement in Artificial Intelligence (AI), specifically deep learning (DL), assistance in precise anatomy identification through semantic segmentation essential for the reliable assessment of growth and neurodevelopment, and detection of structural abnormalities have been p…
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes
ShanghaiTech University（上海科技大学）；Fudan University（复旦大学）；Obstetrics and Gynecology Hospital of Fudan University（复旦大学附属妇产科医院）；Northwestern Polytechnical University（西北工业大学）；Shanghai United Imaging Intelligence company Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
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…
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…
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…
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…
Origin Health India（印度Origin Health公司）；Bangalore Fetal Medicine Center（班加罗尔胎儿医学中心）；Prime Imaging and Prenatal Diagnostics（Prime影像与产前诊断中心）；Kasturba Medical College（卡斯图尔巴医学院）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；Origin Health（Origin Health公司）
Multiple studies have demonstrated that obtaining standardized fetal brain biometry from mid-trimester ultrasonography (USG) examination is key for the reliable assessment of fetal neurodevelopment and the screening of central nervous system (CNS) anomalies. Obtaining these measurements is highly subjective, expertise-driven, and requires years of training experience, limiting quality prenatal care for all pregnant mothers. In this study, we propose a deep learning (DL) approach to compute 3 key fetal brain biometry from the 2D USG images of the transcerebellar plane (TC) through the accurate …
USFetal: Tools for Fetal Brain Ultrasound Compounding
A. I. Virtanen Institute for Molecular Sciences, Faculty of Health Sciences, University of Eastern Finland（A.I.维塔内恩分子科学研究所，健康科学学院，东芬兰大学）；Munich Center for Machine Learning (MCML) at the Technical University of Munich（慕尼黑技术大学机器学习中心（MCML））；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Harvard Medical School and Boston Children’s Hospital（哈佛医学院和波士顿儿童医院）
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…
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, …
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）
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 …
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）
School of Biomedical Engineering & Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学院）；Department of Radiology, University Hospitals Leuven, Belgium（鲁汶大学医院放射科）；Institute for Women’s Health, University College London, UK（伦敦大学学院妇女健康研究所）；Department of Obstetrics and Gynaecology, University Hospitals Leuven, Belgium（鲁汶大学医院妇产科）；Department of Gynecology and Obstetrics, University Hospitals Tanta, Egypt（坦塔大学医院妇产科）；Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna, Austria（维也纳医科大学生物医学成像与图像引导治疗系）
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI, abnormalities exacerbate the variability of the developing brain anatomy compared to non-pathological cases. A small number of abnormal cases, as is typically available in clinical datasets used for training, are unlikely to fairly represent the rich variability of abnormal developing brains. This leads machine learning systems trained by maximizing the average performance to be biased t…
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…
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）
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…
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. …
Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification
Amrita School of Artificial Intelligence（阿米特学校人工智能学院）；Amrita Vishwa Vidyapeetham（阿米特世界大学）；Center for Artificial Intelligence（人工智能中心）；Prince Mohammed Bin Fahd University（普里姆·穆罕默德·本·法赫德大学）
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…
