Segmentation of brain structures in a large dataset of magnetic resonance images (MRI) necessitates automatic segmentation instead of manual tracing. Automatic segmentation methods provide a much-needed alternative to manual segmentation which is both labor intensive and time-consuming. Among brain structures, the hippocampus presents a challenging segmentation task due to its irregular shape, small size, and unclear edges. In this work, we use T1-weighted MRI of 426 subjects to validate the approach and compare three automatic segmentation methods: FreeSurfer, LocalInfo, and ABSS. Four evaluation measures are used to assess agreement between automatic and manual segmentation of the hippocampus. ABSS outperformed the others based on the Dice coefficient, precision, Hausdorff distance, ASSD, RMS, similarity, sensitivity, and volume agreement. Moreover, comparison of the segmentation results, acquired using 1.5T and 3T MRI systems, showed that ABSS is more sensitive than the others to the field inhomogeneity of 3T MRI.
Fast and automatic algorithm to segment Brain (intracranial region) from computed tomography (CT) head images using combination of HU thresholding, identification of intracranial voxels through ray intersection with cranium, special binary erosion and connected components per slice.
Vanderbilt University（范德堡大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Epilepsy is the fourth most common neurological disorder and affects people of all ages worldwide. Deep Brain Stimulation (DBS) has emerged as an alternative treatment option when anti-epileptic drugs or resective surgery cannot lead to satisfactory outcomes. To facilitate the planning of the procedure and for its standardization, it is desirable to develop an algorithm to automatically localize the DBS stimulation target, i.e., Anterior Nucleus of Thalamus (ANT), which is a challenging target to plan. In this work, we perform an extensive comparative study by benchmarking various localization…
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI
Florida Institute of Technology（佛罗里达理工学院）；Daffodil International University（达福德国际大学）；University of North Texas（北德克萨斯大学）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
University of Girona（赫罗纳大学）；University of Burgundy（勃艮第大学）；University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
Alzheimer's disease (AD) is one of the most common public health issues the world is facing today. This disease has a high prevalence primarily in the elderly accompanying memory loss and cognitive decline. AD detection is a challenging task which many authors have developed numerous computerized automatic diagnosis systems utilizing neuroimaging and other clinical data. MRI scans provide high-intensity visible features, making these scans the most widely used brain imaging technique. In recent years deep learning has achieved leading success in medical image analysis. But a relatively little …
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches
AGH University of Krakow（克拉科夫AGH大学）；Universitas Pembangunan Nasional Veteran Yogyakarta（日惹国家发展老兵大学）；Hohai University（河海大学）；Universitas Ahmad Dahlan（艾哈迈德·达赫兰大学）
Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of co…
Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain tissues. Gaussian Bayesian Classifier is exploited to acquire a Gaussian Bayesian Brain Map (GBBM) from the test brain MR images. GBBM is further p…
Shanghai United Imaging Intelligence Co. Ltd（上海联影智能医疗科技有限公司）；Southern Medical University（南方医科大学）
In brain tumor diagnosis and surgical planning, segmentation of tumor regions and accurate analysis of surrounding normal tissues are necessary for physicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates missed normal tissues within the tumor region using a partial convolutional network. Then, a standard normal brain atlas image is registered onto such …
Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Emory University School of Medicine（埃默里大学医学院）；University of Florida School of Medicine（佛罗里达大学医学院）；Brown University（布朗大学）
Automated segmentation of medical imaging is of broad interest to clinicians and machine learning researchers alike. The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image. Image segmentation is a difficult task because of multiparametric heterogeneity within the images, an obstacle that has proven especially challenging in efforts to automate the segmentation of brain lesions from non-contrast head computed tomography (CT). In this research, we have experimented with multiple available deep learning a…
Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires trained expert radiologists. Due to the subjectivity of manual segmentation, there is low inter-rater reliability which can result in diagnostic discrepancies. As the success of many brain tumor treatments depends on early intervention, early detection is paramount. In this context, a fully automated segmentation method for brain tumor segmentation is necessary as an efficient and…
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…
Mayo Clinic（梅奥诊所）；Moffitt Cancer Center and Research Institute（莫菲特癌症中心与研究所）
Whole brain extraction, also known as skull stripping, is a process in neuroimaging in which non-brain tissue such as skull, eyeballs, skin, etc. are removed from neuroimages. Skull striping is a preliminary step in presurgical planning, cortical reconstruction, and automatic tumor segmentation. Despite a plethora of skull stripping approaches in the literature, few are sufficiently accurate for processing pathology-presenting MRIs, especially MRIs with brain tumors. In this work we propose a deep learning approach for skull striping common MRI sequences in oncology such as T1-weighted with ga…
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…
Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semi-automatic methods have become the preferred type of medical image segmentation. We present a hybrid, semi-automatic segmentation method in 3D that integrates both region-based and boundary-based procedures. Our method differs from previous hybrid methods in that we perform region-based and boundary-based approaches separately, which allows for…
TU Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Klinikum Rechts der Isar（Rechts der Isar 医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amounts of manually segmented training data---a necessity for and pitfall of current supervised Deep Lear…
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects …
Recent advances in brain organoid technology are exciting new ways, which have the potential to change the way how doctors and researchers understand and treat cerebral diseases. Despite the remarkable use of brain organoids derived from human stem cells in new drug testing, disease modeling, and scientific research, it is still heavily time-consuming work to observe and analyze the internal structure, cells, and neural inside the organoid by humans, specifically no standard quantitative analysis method combined growing AI technology for brain organoid. In this paper, an automated computer-ass…
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images (mpMRI)is of great clinical importance, which defines tumour size, shape andappearance and provides abundant information for preoperative diag-nosis, treatment planning and survival prediction. Recent developmentson deep learning have significantly improved the performance of auto-mated medical image segmentation. In this paper, we compare severalstate-of-the-art convolutional neural network models for brain tumouri…
Samsung Medical Center（三星医疗中心）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Sungkyunkwan University（成均馆大学）；Columbia University（哥伦比亚大学）；University of Maryland（马里兰大学）
Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls (n=105) and patients with PD (n=105), multiple systemic atrophy (n=132), and progressive supranuclear palsy (n=69) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard…
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…
Indian Institute of Technology (IIT), Varanasi (BHU)（印度理工学院（贝拿勒斯印度教大学））；National Institute of Technology (NIT), Tiruchirappalli（国立理工学院蒂鲁奇拉帕利分校）
Brain tumour segmentation is an essential task in medical image processing. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumours for cancer diagnosis, from large number of MRI images, is both a difficult and time-consuming task. There is a need for automatic brain tumour image segmentation. The purpose of this project is to provide an automatic brain tumour segmentation method of MRI images to help locate the tumour accurately and quickly.
AI Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学AI·维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）；Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi（费米中心——恩里科·费米物理历史博物馆与研究中心）
Manual segmentation of rodent brain lesions from magnetic resonance images (MRIs) is an arduous, time-consuming and subjective task that is highly important in pre-clinical research. Several automatic methods have been developed for different human brain MRI segmentation, but little research has targeted automatic rodent lesion segmentation. The existing tools for performing automatic lesion segmentation in rodents are constrained by strict assumptions about the data. Deep learning has been successfully used for medical image segmentation. However, there has not been any deep learning approach…
Center for Data Science, New York University（纽约大学数据科学中心）；New York University School of Medicine（纽约大学医学院）
Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard…
Accurate automatic classification of major tissue classes and the cerebrospinal fluid in pediatric MR scans of early childhood brains remains a challenge. A poor and highly variable grey matter and white matter contrast on T1-weighted MR scans of developing brains complicates the automatic categorization of voxels into major tissue classes using state-of-the-art classification methods (Partial Volume Estimation). Varying intensities across brain tissues and possible tissue artifacts further contribute to misclassification. In order to improve the accuracy of automatic detection of major tissue…
CEA, Fundamental Research Division (DRF), Institute Jacob, Molecular Imaging Research Center (MIRCen)（法国原子能与可替代能源委员会（CEA）基础研究部雅各布研究所分子影像研究中心（MIRCen））；WITSEE（WITSEE 公司）
A significant challenge for brain histological data analysis is to precisely identify anatomical regions in order to perform accurate local quantifications and evaluate therapeutic solutions. Usually, this task is performed manually, becoming therefore tedious and subjective. Another option is to use automatic or semi-automatic methods, among which segmentation using digital atlases co-registration. However, most available atlases are 3D, whereas digitized histological data are 2D. Methods to perform such 2D-3D segmentation from an atlas are required. This paper proposes a strategy to automati…
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…
Brain MRI segmentation results should always undergo a quality control (QC) process, since automatic segmentation tools can be prone to errors. In this work, we propose two deep learning-based architectures for performing QC automatically. First, we used generative adversarial networks for creating error maps that highlight the locations of segmentation errors. Subsequently, a 3D convolutional neural network was implemented to predict segmentation quality. The present pipeline was shown to achieve promising results and, in particular, high sensitivity in both tasks.
Brown University（布朗大学）；Brown Alpert Medical School（布朗大学阿尔珀特医学院）
We present a joint graph convolution-image convolution neural network as our submission to the Brain Tumor Segmentation (BraTS) 2021 challenge. We model each brain as a graph composed of distinct image regions, which is initially segmented by a graph neural network (GNN). Subsequently, the tumorous volume identified by the GNN is further refined by a simple (voxel) convolutional neural network (CNN), which produces the final segmentation. This approach captures both global brain feature interactions via the graphical representation and local image details through the use of convolutional filte…
Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the orig…
Beijing Technology and Business University（北京工商大学）
Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The main ideas included that first, two image patches with size of 128 in three dimensions were selected according to glioma information of ground truth labels from the magnetic resonance imaging data of any two patients with the same modality. Next, a tensor in which all elements were independently sampled from…
Ohio University（俄亥俄大学）；Huazhong Normal University（华中师范大学）；University of Kentucky（肯塔基大学）
In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1.5T MRIs. The models include a fully convolutional network (FCN) method and three state-of-the-art super-resolution solutions, ESPCN [26], SRGAN [17] and PRSR [7]. The FCN solution, U-Convert-Net, carries out mapping of 1.5T-to-3T slices through a U-Net-like architecture, with 3D neighborhood information integrated through a multi-view ensemble. The pros and cons of the models, as well the associated evaluation metrics, are measured with experiments an…
Department of Health Technology, Technical University of Denmark（丹麦技术大学健康技术系）；Department of Oncology, The Finsen Center, Rigshospitalet（哥本哈根大学医院芬森中心肿瘤学系）；Department of Clinical Physiology, Nuclear Medicine and PET, Center of Diagnostic Investigation, Rigshospitalet（哥本哈根大学医院诊断研究中心临床生理学、核医学与正电子发射断层扫描科）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院Athinoula A. Martinos生物医学成像中心）
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct biological interpretation: They measure the…
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study
University of Dhaka（达卡大学）；Utah State University（犹他州立大学）；Noakhali Science and Technology University（诺阿卡利科学技术大学）；Daffodil International University（水仙花国际大学）
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）
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…
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations
Department of Health Research（健康研究系）；SINTEF Digital（SINTEF数字）；Department of Physics（物理系）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；Department of Circulation and Medical Imaging（循环与医学成像系）；St. Olavs hospital（圣奥拉夫医院）；Department of Neuromedicine and Movement Science（神经医学与运动科学系）
Reutlingen University（罗伊特林根大学）；Menoufia University（米努夫大学）；University of Ulm（乌尔姆大学）
Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-Attenuated Inversion Recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture; namely DeepSeg for fully automated detection and segmentation of the brain lesion using FLAIR MRI data. Methods: The developed DeepSeg is a modular decou…
Ankara University（安卡拉大学）；Turkish Aerospace（土耳其航空航天工业公司）；Akgun R&D Center（Akgun研发中心）
Medical imaging has been employed to support medical diagnosis and treatment. It may also provide crucial information to surgeons to facilitate optimal surgical preplanning and perioperative management. Essentially, semi-automatic organ and tumor segmentation has been studied by many researchers. Recently, with the development of Deep Learning (DL) algorithms, automatic organ segmentation has been gathered lots of attention from the researchers. This paper addresses to propose the most efficient DL architectures for Liver segmentation by adapting and comparing state-of-the-art DL frameworks, s…
Oslo Metropolitan University（奥斯陆城市大学）；Norwegian University of Science and Technology（挪威科技大学）；Kavli Institute for Systems Neuroscience（卡夫利系统神经科学研究所）
Removing skull artifacts from functional magnetic images (fMRI) is a well understood and frequently encountered problem. Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle human data. Nonetheless, these tools are not equally useful to handle the data derived from animal studies, especially from rodents. This represents a major problem to the field because rodent studies generate larger datasets from larger populations, which implies that preprocessing these images manually to remove the skull becomes a bottleneck in the data analysis pipeline.…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Wilhelmina Children’s Hospital, University Medical Center Utrecht（乌得勒支大学医学中心威廉明娜儿童医院）；Brain Center Rudolf Magnus, University Medical Center Utrecht（乌得勒支大学医学中心鲁道夫·马格努斯脑中心）
Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infant head movements during image acquisition. Methods have been developed to remove or minimize these artifacts during image reconstruction using frequency domain data. However, frequency domain data might not always be available. Hence, in this study we propose a method for removing motion artifacts from the already reconstructed MR scans. The method employs a generative adversarial network…
Inria（法国国家信息与自动化研究所）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎大脑研究所）；Inserm（法国国家健康与医学研究院）；CNRS（法国国家科学研究中心）；AP-HP, Hôpital de la Pitié Salpêtrière（巴黎公立医院集团，皮提耶-萨勒佩特里医院）；AP-HP, WIND department（巴黎公立医院集团，WIND部门）
Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinic…
New Jersey Institute of Technology（新泽西理工学院）；Kessler Foundation（凯斯勒基金会）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；Medical College of Wisconsin（威斯康星医学院）；Rutgers University - Newark（罗格斯大学纽瓦克分校）
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images. Our multi-path model has independent encoders for different modalities containing residual convolutional blocks, weighted multi-path feature fusion from different modalities, and weighted fusion modules to combine encoder and decoder features. Compared to existing 3D CNNs like DeepMedic, 3D U-Net, and AnatomyNet, our networks achieves the highest statistically significant cross-validation accuracy of 60.5% on the large ATLAS benchmark of 220 patients. We also test our model on multi-modal…
Singapore-ETH Center（新加坡-ETH中心）；ETH Zuerich（苏黎世联邦理工学院）；University of Melbourne（墨尔本大学）
Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public software packages for 3D surface generation are evaluated, using 5cm GSD imagery recorded from a UAV. Generated surface models are assessed against point clouds generated from mobile LiDAR and manual stereoscopic measurements. The software packages considered are APS, MICMAC, SURE, Pi…
CNRS, University of Bordeaux, Bordeaux INP（法国国家科学研究中心、波尔多大学、波尔多综合理工学院）
Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. An automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, an automated deep neural network approaches appear as a method of choice to automatic…
New York University（纽约大学）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Riverside Research（河滨研究院）；Skirball Institute of Biomolecular Medicine, New York University School of Medicine（纽约大学医学院斯基尔比尔分子医学研究所）
High-frequency ultrasound (HFU) is well suited for imaging embryonic mice in vivo because it is non-invasive and real-time. Manual segmentation of the brain ventricles (BVs) and whole body from 3D HFU images is time-consuming and requires specialized training. This paper presents a deep-learning-based segmentation pipeline which automates several time-consuming, repetitive tasks currently performed to study genetic mutations in developing mouse embryos. Namely, the pipeline accurately segments the BV and body regions in 3D HFU images of mouse embryos, despite significant challenges due to posi…
Gliomas are the most common and aggressive among brain tumors, which cause a short life expectancy in their highest grade. Therefore, treatment assessment is a key stage to enhance the quality of the patients' lives. Recently, deep convolutional neural networks (DCNNs) have achieved a remarkable performance in brain tumor segmentation, but this task is still difficult owing to high varying intensity and appearance of gliomas. Most of the existing methods, especially UNet-based networks, integrate low-level and high-level features in a naive way, which may result in confusion for the model. Mor…
Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resources for both clinical and research environments. In the last years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labelling at multiscale level and to deal with brain anatomical alterations such as white matter lesions. In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis which densely labe…
Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-sequence 3D imaging. This study demonstrates automated detection and segmentation of brain metastases on multi-sequence MRI using a deep learning approach based on a fully convolution neural network (CNN). In this retrospective study, a total of 156 patients with brain metastases from several primary cancers were included. Pre-therapy MR images (1.5T and 3T) included pre- and post-gadolinium T1-weighted 3D fast spin echo, post-gadolinium T1-weighted 3…
EURECOM（欧洲通信学院）；University of Brasília（巴西利亚大学）；Syrian-Lebanese Hospital（叙利亚-黎巴嫩医院）
Gliomas are the most common malignant brain tumors that are treated with chemoradiotherapy and surgery. Magnetic Resonance Imaging (MRI) is used by radiotherapists to manually segment brain lesions and to observe their development throughout the therapy. The manual image segmentation process is time-consuming and results tend to vary among different human raters. Therefore, there is a substantial demand for automatic image segmentation algorithms that produce a reliable and accurate segmentation of various brain tissue types. Recent advances in deep learning have led to convolutional neural ne…
