Fully automatic identification of AC and PC landmarks on brain MRI using scene analysis

1997 ◽  
Vol 16 (5) ◽  
pp. 610-616 ◽  
Author(s):  
L. Verard ◽  
P. Allain ◽  
J.M. Travere ◽  
J.C. Baron ◽  
D. Bloyet
Keyword(s):  
Brain Mri ◽  
Scene Analysis ◽  
Automatic Identification ◽  
Fully Automatic

scholarly journals A NEW FULLY-AUTOMATIC PROCEDURE FOR THE IDENTIFICATION AND THE COUPLING OF THE OVERTOPPING WAVES

2018 ◽  
pp. 36 ◽  
Author(s):  
Sara Mizar Formentin ◽  
Barbara Zanuttigh
Keyword(s):  
Flow Velocity ◽  
Water Surface ◽  
Automatic Identification ◽  
Practical Relevance ◽  
Threshold Values ◽  
Flow Conditions ◽  
Fully Automatic ◽  
The Individual ◽  
Coupling Algorithm ◽  
Submerged Conditions

This contribution presents a new procedure for the automatic identification of the individual overtopping events. The procedure is based on a zero-down-crossing analysis of the water-surface-elevation signals and, based on two threshold values, can be applied to any structure crest level, i.e. to emerged, zero-freeboard, over-washed and submerged conditions. The results of the procedure are characterized by a level of accuracy comparable to the human-supervised analysis of the wave signals. The procedure includes a second algorithm for the coupling of the overtopping events registered at two consecutive gauges. This coupling algorithm offers a series of original applications of practical relevance, a.o. the possibility to estimate the wave celerities, i.e. the velocities of propagation of the single waves, which could be used as an approximation of the flow velocity in shallow water and broken flow conditions.

scholarly journals Deep reinforcement learning for efficient measurement of quantum devices

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
V. Nguyen ◽  
S. B. Orbell ◽  
D. T. Lennon ◽  
H. Moon ◽  
F. Vigneau ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Double Quantum ◽  
Automatic Identification ◽  
Large Parameter ◽  
Quantum Devices ◽  
Parameter Spaces ◽  
Machine Learning Approach ◽  
Efficient Measurement ◽  
Fully Automatic ◽  
Mean Time

AbstractDeep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.

scholarly journals Prenatal brain MRI samples for development of automatic segmentation, target- recognition and machine-learning algorithms to detect anatomical structures

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 93
Author(s):  
Hugues Gentillon ◽  
Ludomir Stefańczyk ◽  
Michał Strzelecki ◽  
Maria Respondek-Liberska
Keyword(s):  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Fetal Mri ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Automatic Identification ◽  
Sorting Algorithm ◽  
Cross Sectional ◽  
Anatomical Structures

In this data note, we present a sorted pool of fetal magnetic resonance imaging (MRI) specimens, selected for a project seeking to further develop a computer-vision software called MaZda, originally created for magnetic resonance (MR) image analysis. A link to download the samples is provided in the manuscript herein. This data descriptor further explains how and why these fetal MRI samples were selected. Firstly, thousands of cross-sectional images obtained from fetal MRI scans were processed and sorted semi-manually with other software. We did so because a built-in “samplesort” (sorting algorithm) is missing in MaZda version 5. Additionally, the software is unfortunately lacking effective and efficient algorithms to allow automatic identification and segmentation of anatomical structures in fetal MRI samples. Hence, the finals sorting steps were carried out manually via time-consuming methods — i.e. human- visual detection and classifications by the gestational age of pregnancy and the rotational plane of the MR scanner. Thus the latter correlates with the anatomical plane of the mother, rather than the hypothetical plane used to transect the fetus. In brief, we collated these fetal MRI samples in an effort to facilitate future research and discovery, especially to aid the improvement of MaZda.

scholarly journals Machine Learning Framework for Fully Automatic Quality Checking of Rigid and Affine Registrations in Big Data Brain MRI

2020 ◽  
Author(s):  
Sudhakar Tummala ◽  
Niels K. Focke
Keyword(s):  
Machine Learning ◽  
Data Exchange ◽  
Brain Mri ◽  
Structural Mri ◽  
Imaging Data ◽  
Normalized Mutual Information ◽  
Learning Framework ◽  
Fully Automatic ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

ABSTRACTRigid and affine registrations to a common template are the essential steps during pre-processing of brain structural magnetic resonance imaging (MRI) data. Manual quality check (QC) of these registrations is quite tedious if the data contains several thousands of images. Therefore, we propose a machine learning (ML) framework for fully automatic QC of these registrations via local computation of the similarity functions such as normalized cross-correlation, normalized mutual-information, and correlation ratio, and using these as features for training of different ML classifiers. To facilitate supervised learning, misaligned images are generated. A structural MRI dataset consisting of 215 subjects from autism brain imaging data exchange is used for 5-fold cross-validation and testing. Few classifiers such as kNN, AdaBoost, and random forest reached testing F1-scores of 0.98 for QC of both rigid and affine registrations. These tested ML models could be deployed for practical use.

scholarly journals Support Vector Machine-based Spontaneous Intracranial Hypotension Detection on Brain MRI

2021 ◽  
Author(s):  
Philipp G. Arnold ◽  
Emre Kaya ◽  
Marco Reisert ◽  
Niklas Lützen ◽  
Philippe Dovi-Akué ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intracranial Hypotension ◽  
Brain Mri ◽  
Spontaneous Intracranial Hypotension ◽  
Area Under The Curve ◽  
Support Vector ◽  
Venous Sinuses ◽  
Fully Automatic ◽  
Magnetic Resonance Imaging Mri ◽  
Automatic Algorithm

Abstract Background and Purpose To develop a fully automatic algorithm for the magnetic resonance imaging (MRI) identification of patients with spontaneous intracranial hypotension (SIH). Material and Methods A support vector machine (SVM) was trained with structured reports of 140 patients with clinically suspected SIH. Venous sinuses and basal cisterns were segmented on contrast-enhanced T1-weighted MPRAGE (Magnetization Prepared-Rapid Gradient Echo) sequences using a convolutional neural network (CNN). For the segmented sinuses and cisterns, 56 radiomic features were extracted, which served as input data for the SVM. The algorithm was validated with an independent cohort of 34 patients with proven cerebrospinal fluid (CSF) leaks and 27 patients who had MPRAGE scans for unrelated reasons. Results The venous sinuses and the suprasellar cistern had the best discriminative power to separate SIH and non-SIH patients. On a combined score with 2 points, mean SVM score was 1.41 (±0.60) for the SIH and 0.30 (±0.53) for the non-SIH patients (p < 0.001). Area under the curve (AUC) was 0.91. Conclusion A fully automatic algorithm analyzing a single MRI sequence separates SIH and non-SIH patients with a high diagnostic accuracy. It may help to consider the need of invasive diagnostics and transfer to a SIH center.

scholarly journals A fully automatic and robust brain MRI tissue classification method

2003 ◽  
Vol 7 (4) ◽  
pp. 513-527 ◽  
Author(s):  
Chris A. Cocosco ◽  
Alex P. Zijdenbos ◽  
Alan C. Evans
Keyword(s):  
Brain Mri ◽  
Classification Method ◽  
Tissue Classification ◽  
Fully Automatic

An Accurate Automated Local Similarity Factor-Based Neural Tree Approach toward Tissue Segmentation of Newborn Brain MRI

2018 ◽  
Vol 36 (11) ◽  
pp. 1157-1170 ◽  
Author(s):  
Tushar H. Jaware ◽  
K. B. Khanchandani ◽  
Anita Zurani
Keyword(s):  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Adult Brain ◽  
Local Similarity ◽  
Tissue Segmentation ◽  
Similarity Factor ◽  
Newborn Brain ◽  
Fully Automatic ◽  
Tree Approach ◽  
Brain Tissues

Background Segmentation of brain MR images of neonates is a primary step for assessment of brain evolvement. Advanced segmentation techniques used for adult brain MRI are not companionable for neonates, due to extensive dissimilarities in tissue properties and head structure. Existing segmentation methods for neonates utilizes brain atlases or requires manual elucidation, which results into improper and atlas dependent segmentation. Objective The primary objective of this work is to develop fully automatic, atlas free, and robust system to segment and classify brain tissues of newborn infants from magnetic resonance images. Study Design In this study, we propose a fully automatic, atlas-free pipeline based Neural Tree approach for segmentation of newborn brain MRI which utilizes resourceful local resemblance factor such as concerning, connectivity, structure, and relative tissue location. Physical collaboration and uses of an atlas are not required in proposed method and at the same time skirting atlas-associated bias which results in improved segmentation. Proposed technique segments and classify brain tissues both at global and tissue level. Results We examined our results through visual assessment by neonatologists and quantitative comparisons that show first-rate concurrence with proficient manual segmentations. The implementation results of the proposed technique provided a good overall accuracy of 91.82% for the segmentation of brain tissues as compared with other methods. Conclusion The pipelined-based neural tree approach along with local similarity factor segments and classify brain tissues. The proposed automated system have higher dice similarity coefficient as well as computational speed.

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