Boosting Skull-Stripping Performance for Pediatric Brain Images
1 Athinoula A.\ Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA 2 Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA 3 Department of Radiology, Harvard Medical School, Boston, MA 02115, USA 4 Division of Health Sciences；Technology, MIT, Cambridge, MA 02139, USA 5 Computer Science \& Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
William Kelley, Nathan Ngo, Adrian V. Dalca, Bruce Fischl, Lilla Zöllei, Malte Hoffmann
备注：
5 pages, 5 figures, 1 table, skull-stripping, brain extraction, newborn, infant, toddler, pediatric MRI, machine learning, accepted by the IEEE International Symposium on Biomedical Imaging
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.
SST-DUNet: Automated preclinical functional MRI skull stripping using Smart Swin Transformer and Dense UNet
School of Information Technology, Carleton University（卡尔顿大学信息技术学院）；Center for Translational NeuroImaging (CTNI), Northeastern University（东北大学转化神经影像学中心）；College of Science and Humanity Studies, Prince Sattam Bin Abdulaziz University（萨塔姆·本·阿卜杜勒阿齐兹王子大学科学与人文研究学院）；Tessellis Ltd.（泰塞利斯有限公司）
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工学院健康科学与技术部）
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-ba…
Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification
Department of Neurology, Medical University of Graz（格拉茨医科大学神经科）；Institute of Biomedical Imaging, Graz University of Technology（格拉茨技术大学生物医学成像研究所）；BioTechMed-Graz（格拉茨BioTechMed）
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…
The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimat…
MindGrab for BrainChop: Fast and Accurate Skull Stripping for Command Line and Browser
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三机构神经影像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Harvard University（哈佛大学）；University of South Carolina（南卡罗来纳大学）
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Drexel University（德雷塞尔大学）
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images. A lot of deep learn-ing neural network based methods have been developed toachieve higher accuracy. Since the 3D deep learning modelssuffer from high computational cost and are subject to GPUmemory limit challenge, a variety of 2D deep learning meth-ods have been developed. However, existing 2D deep learn-ing methods are not equipped to effectively capture 3D se-mantic information that is needed to achieve higher accuracy.In this paper, we propose a context-encoding method to em-power the 2D …
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…
Federal University of Santa Catarina（圣卡塔琳娜联邦大学）；Santa Luzia Hospital（圣卢西亚医院）
State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation, brain extraction (BE, also known as skull-stripping) and manual correction by an expert. However, for clinical practice, this last step is tedious and time-consuming and, therefore, not always feasible, resulting in skull-stripping faults that can negatively impact the tumor segmentation quality. Still, the extent of this impact has never been measured for any of the many diff…
Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weakly supervised learning approach, called Neural Pre-processing (NPP), for solving all three sub-tasks simultaneously via a neural network, trained on a large dataset without individual sub-task supervision. Because the overall objective is highly under-constrained, we explicitly disentangle geometric-preserving intensity mapping (skull-stripping and intensity normalization) and spatial transformation (spatial normaliz…
The University of Tokyo, Tokyo, Japan（日本 东京 东京大学）；KTH Royal Institute of Technology, Stockholm, Sweden（瑞典 斯德哥尔摩 KTH皇家理工学院）
Whole-brain surface extraction is an essential topic in medical imaging systems as it provides neurosurgeons with a broader view of surgical planning and abnormality detection. To solve the problem confronted in current deep learning skull stripping methods lacking prior shape information, we propose a new network architecture that incorporates knowledge of signed distance fields and introduce an additional Laplacian loss to ensure that the prediction results retain shape information. We validated our newly proposed method by conducting experiments on our brain magnetic resonance imaging datas…
Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected th…
Negligible effect of brain MRI data preprocessing for tumor segmentation
Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））；Skolkovo Institute of Science and Technology（斯科尔科沃科技学院）；National Medical Research Center for Neurosurgery（国家神经外科医学研究中心）
