Background and Purpose—The authors present an overview of the current evidence and management recommendations for evaluation and treatment of adults with acute ischemic stroke. The intended audienc...

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Guidelines for the Early Management of Patients With Acute Ischemic Stroke A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Background and Purpose- The purpose of these guidelines is to provide an up-to-date comprehensive set of recommendations in a single document for clinicians caring for adult patients with acute arterial ischemic stroke. The intended audiences are prehospital care providers, physicians, allied health professionals, and hospital administrators. These guidelines supersede the 2013 Acute Ischemic Stroke (AIS) Guidelines and are an update of the 2018 AIS Guidelines. Methods- Members of the writing group were appointed by the American Heart Association (AHA) Stroke Council's Scientific Statements Oversight Committee, representing various areas of medical expertise. Members were not allowed to participate in discussions or to vote on topics relevant to their relations with industry. An update of the 2013 AIS Guidelines was originally published in January 2018. This guideline was approved by the AHA Science Advisory and Coordinating Committee and the AHA Executive Committee. In April 2018, a revision to these guidelines, deleting some recommendations, was published online by the AHA. The writing group was asked review the original document and revise if appropriate. In June 2018, the writing group submitted a document with minor changes and with inclusion of important newly published randomized controlled trials with >100 participants and clinical outcomes at least 90 days after AIS. The document was sent to 14 peer reviewers. The writing group evaluated the peer reviewers' comments and revised when appropriate. The current final document was approved by all members of the writing group except when relationships with industry precluded members from voting and by the governing bodies of the AHA. These guidelines use the American College of Cardiology/AHA 2015 Class of Recommendations and Level of Evidence and the new AHA guidelines format. Results- These guidelines detail prehospital care, urgent and emergency evaluation and treatment with intravenous and intra-arterial therapies, and in-hospital management, including secondary prevention measures that are appropriately instituted within the first 2 weeks. The guidelines support the overarching concept of stroke systems of care in both the prehospital and hospital settings. Conclusions- These guidelines provide general recommendations based on the currently available evidence to guide clinicians caring for adult patients with acute arterial ischemic stroke. In many instances, however, only limited data exist demonstrating the urgent need for continued research on treatment of acute ischemic stroke.

Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association.

In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

https://spiral.imperial.ac.uk/bitstream/10044/1/53839/13/1-s2.0-S1361841516301839-main.pdf

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

Permission from the ethics committee and informed consent were obtained. The purpose of this study was to prospectively evaluate a method developed for the noninvasive assessment of muscle metabolites during exercise. Hydrogen 1 magnetic resonance (MR) spectroscopy peaks were measured during tetanic isometric muscle contraction imposed by supramaximal repetitive nerve stimulation. The kinetics of creatine-phosphocreatine and acetylcarnitine signal changes (P < .001) could be assessed continuously before, during, and after exercise. The control peak (trimethylammonium compounds), which served as an internal reference, did not change. This technique-that is, functional MR spectroscopy-opens the possibility for noninvasive diagnostic muscle metabolite testing in a clinical setting.

Muscle metabolites: functional MR spectroscopy during exercise imposed by tetanic electrical nerve stimulation.

To assess if intratumoral blood circulation parameters from dynamic susceptibility contrast (DSC) MRI and dynamic CT deliver comparable results and to compare tumor-related changes in regional cerebral blood flow (rCBF) and regional cerebral blood volume (rCBV) with arterial, intratumoral, and venous transition detected with digital subtraction angiography (DSA). Ten patients with cerebral gliomas were prospectively studied with DSC-MRI, dynamic CT and DSA. Tumor areas were segmented and perfusion maps for rCBF, rCBV, and mean transit time (MTT) were computed from DSC-MRI and dynamic CT. Arterial circulation time (ACT), intermediate circulation time (ICT), and venous appearance time (VAT) were measured with DSA. Asymmetry indices (AIs) were calculated for MRI- and CT-based perfusion values, for ICT and VAT and compared among each other. DSC-MRI and dynamic CT yielded comparable AI values for rCBF (MRI: 39.5 ± 20.4, CT: 36.0 ± 17.9, Pearson's correlation r2 = 0.91) and rCBV (MRI: 44.6 ± 20.9 vs. CT: 40.9 ± 16.3, r2 = 0.84). The MTT AI (MRI: –4.7 ± 11.2 vs. CT: –0.5 ± 10.4, r2 = 0.47) showed only a weak correlation. ICT correlated with rCBV (ICT: 38.4 ± 14.7, r2 = 0.59, and dynamic CT: r2 = 0.81) and VAT with rCBF (VAT: 31.7 ± 17.6, r2 = 0.73, and dynamic CT: r2 = 0.87), but not with MTT. CT and MRI methods provide consistent information about tumor vascularity of cerebral gliomas in accordance with DSA.

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Vascular Dynamics of Cerebral Gliomas Investigated with Selective Catheter Angiography, Perfusion CT and MRI

PURPOSE

To improve the detection of peritumoral changes in GBM patients by exploring the relation between MRSI information and the distance to the solid tumor volume (STV) defined using structural MRI (sMRI).

METHODS

Twenty-three MRSI studies (PRESS, TE 135 ms) acquired from different patients with untreated GBM were used in this study. For each MRSI examination, the STV was identified by segmenting the corresponding sMRI images using BraTumIA, an automatic segmentation method. The relation between different metabolite ratios and the distance to STV was analyzed. A regression forest was trained to predict the distance from each voxel to STV based on 14 metabolite ratios. Then, the trained model was used to determine the expected distance to tumor (EDT) for each voxel of the MRSI test data. EDT maps were compared against sMRI segmentation.

RESULTS

The features showing abnormal values at the longest distances to the tumor were: %NAA, Glx/NAA, Cho/NAA, and Cho/Cr. These four features were also the most important for the prediction of the distances to STV. Each EDT value was associated with a specific metabolic pattern, ranging from normal brain tissue to actively proliferating tumor and necrosis. Low EDT values were highly associated with malignant features such as elevated Cho/NAA and Cho/Cr.

CONCLUSION

The proposed method enables the automatic detection of metabolic patterns associated with different distances to the STV border and may assist tumor delineation of infiltrative brain tumors such as GBM.

On the relation between MR spectroscopy features and the distance to MRI-visible solid tumor in GBM patients.

Clinical use of MRSI is limited by the level of experience required to properly translate MRSI examinations into relevant clinical information. To solve this, several methods have been proposed to automatically recognize a predefined set of reference metabolic patterns. Given the variety of metabolic patterns seen in glioma patients, the decision on the optimal number of patterns that need to be used to describe the data is not trivial. In this paper, we propose a novel framework to (1) separate healthy from abnormal metabolic patterns and (2) retrieve an optimal number of reference patterns describing the most important types of abnormality. Using 41 MRSI examinations (1.5 T, PRESS, TE 135 ms) from 22 glioma patients, four different patterns describing different types of abnormality were detected: edema, healthy without Glx, active tumor and necrosis. The identified patterns were then evaluated on 17 MRSI examinations from nine different glioma patients. The results were compared against BraTumIA, an automatic segmentation method trained to identify different tumor compartments on structural MRI data. Finally, the ability to predict future contrast enhancement using the proposed approach was also evaluated.

Analysis of metabolic abnormalities in high-grade glioma using MRSI and convex NMF

Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.

Johannes Slotboom

Papers

Muscle metabolites: functional MR spectroscopy during exercise imposed by tetanic electrical nerve stimulation.

Vascular Dynamics of Cerebral Gliomas Investigated with Selective Catheter Angiography, Perfusion CT and MRI

On the relation between MR spectroscopy features and the distance to MRI-visible solid tumor in GBM patients.

Analysis of metabolic abnormalities in high-grade glioma using MRSI and convex NMF

A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases