Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.g., vasculature). We propose a BM-detection framework using a single-sequence gadolinium-enhanced T1-weighted 3D MRI dataset. The framework focuses on detection of smaller (< 15 mm) BM lesions and consists of: (1) candidate-selection stage, using Laplacian of Gaussian approach for highlighting parts of a MRI volume holding higher BM occurrence probabilities, and (2) detection stage that iteratively processes cropped region-of-interest volumes centered by candidates using a custom-built 3D convolutional neural network ("CropNet"). Data is augmented extensively during training via a pipeline consisting of random gamma correction and elastic deformation stages; the framework thereby maintains its invariance for a plausible range of BM shape and intensity representations. This approach is tested using five-fold cross-validation on 217 datasets from 158 patients, with training and testing groups randomized per patient to eliminate learning bias. The BM database included lesions with a mean diameter of ~5.4 mm and a mean volume of ~160 mm3. For 90% BM-detection sensitivity, the framework produced on average 9.12 false-positive BM detections per patient (standard deviation of 3.49); for 85% sensitivity, the average number of false-positives declined to 5.85. Comparative analysis showed that the framework produces comparable BM-detection accuracy with the state-of-art approaches validated for significantly larger lesions.
Massachusetts General Hospital（麻省总医院）；Massachusetts Institute of Technology（麻省理工学院）；National Taiwan University Hospital（台湾大学医院）；Vysioneer Inc.（Vysioneer公司）
Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This wo…
The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset
University of California San Francisco（加州大学旧金山分校）；University of California, San Diego（加州大学圣地亚哥分校）；University of Pennsylvania（宾夕法尼亚大学）；Duke University（杜克大学）
Early detection of brain metastases (BM) is one of the determining factors for the successful treatment of patients with cancer; however, the accurate detection of small BM lesions (< 15mm) remains a challenging task. We previously described a framework for the detection of small BM in single-sequence gadolinium-enhanced T1-weighted 3D MRI datasets. It combined classical image processing (IP) with a dedicated convolutional neural network, taking approximately 30 seconds to process each dataset due to computation-intensive IP stages. To overcome the speed limitation, this study aims to reformul…
Oslo University Hospital（奥斯陆大学医院）；Stanford University（斯坦福大学）
The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI sequences using an input dropout layer, thus simulating the scenario of missing MRI data by training on the full set and all possible subsets of the input data. This retrospective, multi-center study, evaluated 165 patients with brain metastases. A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested …
A medical AI system's generalizability describes the continuity of its performance acquired from varying geographic, historical, and methodologic settings. Previous literature on this topic has mostly focused on "how" to achieve high generalizability with limited success. Instead, we aim to understand "when" the generalizability is achieved: Our study presents a medical AI system that could estimate its generalizability status for unseen data on-the-fly. We introduce a latent space mapping (LSM) approach utilizing Frechet distance loss to force the underlying training data distribution into a …
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…
Universitätsklinikum Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希-亚历山大大学（FAU））；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-EMN综合癌症中心（CCC ER-EMN））
Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting t…
FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation
Digital Medical Research Center, School of Basic Medical Science, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention（上海市医学影像计算与计算机辅助介入重点实验室）
The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. We previously developed a framework for detecting small BM (with diameters of less than 15mm) in T1-weighted Contrast-Enhanced 3D Magnetic Resonance images (T1c) to assist medical experts in this time-sensitive and high-stakes task. The framework utilizes a dedicated convolutional neural network (CNN) trained using labeled T1c data, where the ground truth BM segmentations were provided by a radiologist. This study aims to advance the framework with a noisy student-based …
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Wake Forest School of Medicine（维克森林大学医学院）
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow fo…
Background: Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective: This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods: Among medical imaging methods, brains MRI segmentation is important due to the high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues a…
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.
Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Universitätsklinikum Erlangen（埃尔朗根大学医院）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-EMN综合癌症中心）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；University of Zurich（苏黎世大学）；University Hospital Zurich（苏黎世大学医院）
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医疗技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics GmbH公司）；Hamburg University of Technology（汉堡工业大学）
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these methods only rely on the visual appearance of healthy brain anatomy for anomaly detection. Another biomarker for abnormal brain development is the deviation between the brain age and the chronological age, which is unexplored in combination with …
Deep learning builds the foundation for many medical image analysis tasks where neuralnetworks are often designed to have a large receptive field to incorporate long spatialdependencies. Recent work has shown that large receptive fields are not always necessaryfor computer vision tasks on natural images. We explore whether this translates to certainmedical imaging tasks such as age and sex prediction from a T1-weighted brain MRI scans.
Silesian University in Opava（奥帕瓦西里西亚大学）；University of Oxford（牛津大学）；Ostrava University（俄斯特拉发大学）
Matching MRI brain images between patients or mapping patients' MRI slices to the simulated atlas of a brain is key to the automatic registration of MRI of a brain. The ability to match MRI images would also enable such applications as indexing and searching MRI images among multiple patients or selecting images from the region of interest. In this work, we have introduced robustness, accuracy and cumulative distance metrics and methodology that allows us to compare different techniques and approaches in matching brain MRI of different patients or matching MRI brain slice to a position in the …
LPIXEL Inc.（LPIXEL公司）；University of Cambridge（剑桥大学）；National Institute of Informatics（国立信息学研究所）；National Center for Global Health and Medicine（国立国际医疗研究中心）；Juntendo University（顺天堂大学）；ELTE Eötvös Loránd University（厄特沃什·罗兰大学）；The University of Tokyo（东京大学）
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with eith…
Hamburg University of Technology（汉堡工业大学）；Jung Diagnostics GmbH（荣格诊断有限公司）
The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent di…
University of Tübingen（图宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution. In this work, we propose to address this challenge with composable, multiscale morphological transformations in a variational autoencoder (VAE) framework. These transformations are applied to a chosen reference brain image to generate MRI volumes, equipping the model with strong anatomical inductive biases. We structure the VAE latent space in a way such that the model covers the data distribution sufficiently well. We show substantial perfor…
Image-based brain cancer prediction models, based on radiomics, quantify the radiologic phenotype from magnetic resonance imaging (MRI). However, these features are difficult to reproduce because of variability in acquisition and preprocessing pipelines. Despite evidence of intra-tumor phenotypic heterogeneity, the spatial diversity between different slices within an MRI scan has been relatively unexplored using such methods. In this work, we propose a deep anchor attention aggregation strategy with a Vision Transformer to predict survival risk for brain cancer patients. A Deep Anchor Attentio…
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…
The Hong Kong University of Science and Technology（香港科技大学）
Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are not capable of registering images with pathologies. In this paper, we propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a multi-level affine registration, 2) a conditional deep Lapla…
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医学技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics 有限公司）
Purpose. Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning. While a wide range of methods for UAD have been proposed, these methods are mostly 2D and only learn from MRI slices, disregarding that brain lesions are inherently 3D and the spatial context of MRI volumes remains unexploited. Metho…
Magnetic Resonance Imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools have resulted in the rapid increase of DL models and subsequent peer-reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment into the clinic building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL t…
Magnetic Resonance Imaging (MRI) is one of the most flexible and powerful medical imaging modalities. This flexibility does however come at a cost; MRI images acquired at different sites and with different parameters exhibit significant differences in contrast and tissue appearance, resulting in downstream issues when quantifying brain anatomy or the presence of pathology. In this work, we propose to combine multiparametric MRI-based static-equation sequence simulations with segmentation convolutional neural networks (CNN), to make these networks robust to variations in acquisition parameters.…
Automated brain tumor detection in MRI images using CNN and ResNet architectures
Department of Computer Science and Engineering, The National Institute of Engineering, Mysuru, Karnataka, India（计算机科学与工程系，国家工程学院，mysuru，卡纳塔克邦，印度）；Department of MCA, The National Institute of Engineering, Mysuru, Karnataka, India（MCA系，国家工程学院，mysuru，卡纳塔克邦，印度）；Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India（计算机科学与工程系，维迪瓦尔达卡学院，mysuru，卡纳塔克邦，印度）；Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India（电子工程系，维迪瓦尔达卡学院，mysuru，卡纳塔克邦，印度）
Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produ…
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…
Hosei University（法政大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
Increasing numbers of MRI brain scans, improvements in image resolution, and advancements in MRI acquisition technology are causing significant increases in the demand for and burden on radiologists' efforts in terms of reading and interpreting brain MRIs. Content-based image retrieval (CBIR) is an emerging technology for reducing this burden by supporting the reading of medical images. High dimensionality is a major challenge in developing a CBIR system that is applicable for 3D brain MRIs. In this study, we propose a system called disease-oriented data concentration with metric learning (DDC…
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）
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…
University of Calgary（卡尔加里大学）；Hotchkiss Brain Institute, University of Calgary（卡尔加里大学霍奇基斯脑研究所）；Foothills Medical Center（山麓医学中心）；Radboud University Medical Center（拉德堡德大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；University of Amsterdam（阿姆斯特丹大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；University of Campinas（坎皮纳斯大学）
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The Multi-Coil Magnetic Resonance Image (MC-MRI) Reconstruction Challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-w…
Low-Cost and Detunable Wireless Resonator Glasses for Enhanced Eye MRI with Concurrent High-Quality Whole Brain MRI
Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center（范德堡大学成像科学研究所，范德堡大学医学中心）；Department of Electrical and Computer Engineering, Vanderbilt University（范德堡大学电气与计算机工程系）；Philips（飞利浦）；Department of Biomedical Engineering, Vanderbilt University（范德堡大学生物医学工程系）；Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center（范德堡大学医学中心放射学与放射科学系）
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院Martinos生物医学影像中心）；Massachusetts Institute of Technology（麻省理工学院）；Cornell University（康奈尔大学）；University College London（伦敦大学学院）
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To…
SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions
Dept. of Math and Computer Science（数学与计算机科学系）；Department of Neuroinflammation（神经炎症系）；School of Physics, Engineering and CS（物理、工程与计算机科学学校）；University of Hertfordshire（赫特福德大学）
