Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans
COEP Technological University（COEP科技大学）
Anway S. Pimpalkar, Rashmika K. Patole, Ketaki D. Kamble, Mahesh H. Shindikar
备注：
Research Article submitted to the 2nd International Conference on Biomedical Engineering Science and Technology: Roadway from Laboratory to Market, at the National Institute of Technology Raipur, Chhattisgarh, India
Skull Stripping is a requisite preliminary step in most diagnostic neuroimaging applications. Manual Skull Stripping methods define the gold standard for the domain but are time-consuming and challenging to integrate into processing pipelines with a high number of data samples. Automated methods are an active area of research for head MRI segmentation, especially deep learning methods such as U-Net architecture implementations. This study compares Vanilla, Residual, and Dense 2D U-Net architectures for Skull Stripping. The Dense 2D U-Net architecture outperforms the Vanilla and Residual counterparts by achieving an accuracy of 99.75% on a test dataset. It is observed that dense interconnections in a U-Net encourage feature reuse across layers of the architecture and allow for shallower models with the strengths of a deeper network.
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）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to …
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）
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…
University of Campinas（坎皮纳斯大学）；University of Calgary（卡尔加里大学）；Alberta Health Services（阿尔伯塔卫生服务局）
Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required in the training stage. Usually, manual annotation is considered to be the "gold standard". However, medical imaging datasets that include expert manual segmentation are scarce as this step is time-consuming, and therefore expensive. Moreover, single-rater manual annotation is most often used in data-driven approaches making the network optimal with respect to only that single expert. In this work, we propose a CNN for brain extraction in magnetic resonance (MR) imaging, that is full…
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
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…
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 …
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…
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…
KTH Royal Institute of Technology（KTH皇家理工学院）；Science for Life Laboratory（生命科学实验室）
Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-specific abnormalities such as noise patterns. Irrespective of the source, artifacts can not only render a scan useless, but can potentially induce misdiagnoses if left unnoticed. For instance, an artifact may masquerade as a tumor or other abnormality. Retrospective artifact correction (RAC) is concerned with removing artifacts after the scan has already been taken. In this work, …
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学A.I.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）
We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory r…
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…
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…
Segmentation of Gray Matters and White Matters from Brain MRI data
Department of Computer Science and Engineering（计算机科学与工程系）；Waseda University（早稻田大学）；Department of Neurosurgery（神经外科）；Teraoka Memorial Hospital（寺田纪念医院）；Department of Radiology（放射科）；Nippon Medical School Hospital（日本医学大学医院）；Tokyo Rosai Hospital（东京罗赛医院）
University College London（伦敦大学学院）；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）
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e.g., MP-RAGE). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing ("thick slice") in clinical settings every year. The inability to quantitatively analyze these scans hinders the adoption of quantitative neuroimaging in healthcare, and precludes research studies that could attain huge sample sizes and h…
