Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Northeastern University（东北大学）；Nanjing University of Science and Technology（南京理工大学）；Nanjing University（南京大学）；University of Virginia（弗吉尼亚大学）；Children’s National Medical Center（国立儿童医学中心）；Sungkyunkwan University（成均馆大学）；Southern Medical University（南方医科大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；Concordia University（康考迪亚大学）；Xiamen University（厦门大学）；Vanderbilt University（范德堡大学）；Stanford University（斯坦福大学）；Emory University（埃默里大学）；University of Jinan（济南大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Korea University（高丽大学）
Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying Wei, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi… 展开作者
Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying Wei, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang
发表信息：
IEEE Transactions on Medical Imaging, 40(5), 1363-1376, 2021
To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. Training/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participating in iSeg-2019. We review the 8 top-ranked teams by detailing their pipelines/implementations, presenting experimental results and evaluating performance in terms of the whole brain, regions of interest, and gyral landmark curves. We also discuss their limitations and possible future directions for the multi-site issue. We hope that the multi-site dataset in iSeg-2019 and this review article will attract more researchers on the multi-site issue.
LoCI-DiffCom: Longitudinal Consistency-Informed Diffusion Model for 3D Infant Brain Image Completion
School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trail Center（上海临床研究中心）
University of Science, Ho Chi Minh City（胡志明市自然科学大学）；John von Neumann Institute, Ho Chi Minh City（胡志明市约翰·冯·诺依曼研究所）；Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）；University of Arkansas, Fayetteville（阿肯色大学费耶特维尔分校）
While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex due to the extremely low intensity contrast between tissues at around 6-9 months of age as well as amplified noise, myelination, and incomplete volume. In this paper, we tackle those limitations by developing a new deep learning model, named DAM-AL, which contains two main contributions, i.e., dilated attention mechanism and hard-case attention loss. Our DAM-AL netw…
Rewiring Development in Brain Segmentation: Leveraging Adult Brain Priors for Enhancing Infant MRI Segmentation
Department of Information Engineering University of Brescia (Italy)（信息工程系，布雷西亚大学）；School of Psychology & Neuroscience University of Glasgow (UK)（心理学与神经科学学院，格拉斯哥大学）；Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health University of Brescia (Italy)（医学与外科专科、放射科学与公共卫生系，布雷西亚大学）
Department of Biomedical Engineering, University of Tennessee Health Science Center（田纳西大学健康科学中心生物医学工程系）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Enanta Pharmaceuticals（Enanta制药公司）
Determining if the brain is developing normally is a key component of pediatric neuroradiology and neurology. Brain magnetic resonance imaging (MRI) of infants demonstrates a specific pattern of development beyond simply myelination. While radiologists have used myelination patterns, brain morphology and size characteristics to determine age-adequate brain maturity, this requires years of experience in pediatric neuroradiology. With no standardized criteria, visual estimation of the structural maturity of the brain from MRI before three years of age remains dominated by inter-observer and intr…
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Cape Town Faculty of Health Sciences（开普敦大学健康科学学院）；Wayne State University School of Medicine（韦恩州立大学医学院）；MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）
Skullstripping is defined as the task of segmenting brain tissue from a full head magnetic resonance image~(MRI). It is a critical component in neuroimage processing pipelines. Downstream deformable registration and whole brain segmentation performance is highly dependent on accurate skullstripping. Skullstripping is an especially challenging task for infant~(age range 0--18 months) head MRI images due to the significant size and shape variability of the head and the brain in that age range. Infant brain tissue development also changes the $T_1$-weighted image contrast over time, making consis…
University of Tennessee Health Science Center（田纳西大学健康科学中心）；Le Bonheur Children’s Hospital（勒邦赫尔儿童医院）；University of Memphis（孟菲斯大学）
Human brain development is rapid during infancy and early childhood. Many disease processes impair this development. Therefore, brain developmental age estimation (BDAE) is essential for all diseases affecting cognitive development. Brain magnetic resonance imaging (MRI) of infants shows brain growth and morphologic patterns during childhood. Therefore, we can estimate the developmental age from brain images. However, MRI analysis is time-consuming because each scan contains millions of data points (voxels). We investigated the three-dimensional convolutional neural network (3D CNN), a deep le…
University of Tennessee Health Science Center（田纳西大学健康科学中心）；Children's Foundation Research Institute at LeBonheur Children's Hospital（勒邦赫儿童医院儿童基金会研究所）；LeBonheur Children's Hospital（勒邦赫儿童医院）；University of Tennessee School of Medicine（田纳西大学医学院）
The ability to determine if the brain is developing normally is a key component of pediatric neuroradiology and neurology. Brain magnetic resonance imaging (MRI) of infants demonstrates a specific pattern of development beyond simply myelination. While radiologists have used myelination patterns, brain morphology and size characteristics in determining if brain maturity matches the chronological age of the patient, this requires years of experience with pediatric neuroradiology. Due to the lack of standardized criteria, estimation of brain maturity before age three remains fraught with interob…
Myelination plays an important role in the neurological development of infant brain and MRI can visualize the myelination extension as T1 high and T2 low signal intensity at white matter. We tried to construct a convolutional neural network machine learning model to estimate the myelination. Eight layers CNN architecture was constructed to estimate the subjects age with T1 and T2 weighted image at 5 levels associated with myelin maturation in 119 subjects up to 24 months. CNN model learned with all age dataset revealed a strong correlation between the estimated age and the corrected age and th…
Nanjing University of Information Science and Technology（南京信息工程大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Shandong University (Weihai)（山东大学（威海））
Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and fre…
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, bec…
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…
Columbia University（哥伦比亚大学）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）
Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brain structures develop across stages of life. Developmental scientists are particularly interested in the first few years of neurodevelopment. Despite the success of MRI collection and analysis for adults, it is a challenge for researchers to collect high-quality multimodal MRIs from developing infants mainly because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still, and a lack of analysis approaches. These challenges often lead to a significant reduc…
Xidian University（西安电子科技大学）；The First Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第一附属医院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PWML effectively has become one of the active topics in medical image segmentation in recent years. In this paper, we construct an efficient two-stage PWML semantic segmentation network based on the characteristics of the lesion, called refined segmentation R-CNN (RS RCNN). We propose a heuristic RPN (H-RPN) which can utilize surrounding information around the PWMLs for heuristic segmentation. Also, we design a lightwe…
Building of atlases plays a crucial role in the analysis of brain images. In scenarios where early growth, aging or disease trajectories are of key importance, longitudinal atlases become necessary as references, most often created from cross-sectional data. New opportunities will be offered by creating longitudinal brain atlases from longitudinal subject-specific image data, where explicit modeling of subject's variability in slope and intercept leads to a more robust estimation of average trajectories but also to estimates of confidence bounds. This work focuses on a framework to build a con…
Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains
Lausanne University Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）
Image normalization is a building block in medical image analysis. Conventional approaches are customarily utilized on a per-dataset basis. This strategy, however, prevents the current normalization algorithms from fully exploiting the complex joint information available across multiple datasets. Consequently, ignoring such joint information has a direct impact on the performance of segmentation algorithms. This paper proposes to revisit the conventional image normalization approach by instead learning a common normalizing function across multiple datasets. Jointly normalizing multiple dataset…
Dual Attention Residual U-Net for Accurate Brain Ultrasound Segmentation in IVH Detection
Chongqing Electric Power College（重庆电力学院）；Chongqing Metropolitan College of Science and Technology（重庆市级科学研究院）；Xinqiao Hospital, Army Medical University（新桥医院，军队医学大学）
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Hangzhou Dianzi University（杭州电子科技大学）；Shandong University（山东大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Leicester（莱斯特大学）；Communication University of Zhejiang（浙江传媒学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Skull stripping is a crucial prerequisite step in the analysis of brain magnetic resonance images (MRI). Although many excellent works or tools have been proposed, they suffer from low generalization capability. For instance, the model trained on a dataset with specific imaging parameters cannot be well applied to other datasets with different imaging parameters. Especially, for the lifespan datasets, the model trained on an adult dataset is not applicable to an infant dataset due to the large domain difference. To address this issue, numerous methods have been proposed, where domain adaptatio…
Objective: Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF (before and after surgery, i.e. pre-op vs. postop) plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-operative (post-op) computational tomographic (CT)-scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most inte…
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Department of Radiology, West China Second University Hospital, Sichuan University（四川大学华西第二医院放射科）；School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University（清华大学医学院生物医学工程系）；Department of Radiology, Sichuan Provincial Woman’s and Children’s Hospital, The Affiliated Women’s and Children’s Hospital of Chengdu Medical College（四川省妇幼保健院放射科，成都医学院附属妇幼医院）；Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China（成都妇女儿童中央医院，电子科技大学医学院）；Department of Radiology, The Third Affiliated Hospital of Zhengzhou University（郑州大学第三附属医院放射科）；Qujing Maternal and Child Health Hospital, Qujing, China（曲靖 maternal and child health hospital, Qujing, China）
