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Convolution Properties II01:17

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The important convolution properties include width, area, differentiation, and integration properties.
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Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
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Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
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Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.

Refaat E Gabr1, Ivan Coronado1, Melvin Robinson2

  • 1Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA.

Multiple Sclerosis (Houndmills, Basingstoke, England)
|June 14, 2019
PubMed
Summary

Deep learning accurately segments brain tissues and lesions in multiple sclerosis patients using fully convolutional neural networks (FCNNs). This automated approach shows high performance across large datasets, aiding in MS research.

Keywords:
Deep learningartificial intelligencetissue classificationwhite matter lesions

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Multiple sclerosis (MS) is a chronic neurological disease characterized by white matter lesions.
  • Accurate segmentation of brain tissues and MS lesions is crucial for disease monitoring and treatment evaluation.
  • Current segmentation methods can be time-consuming and may lack consistency.

Purpose of the Study:

  • To evaluate the performance of a deep learning (DL) model, specifically a fully convolutional neural network (FCNN), for segmenting brain tissues in a large cohort of MS patients.
  • To assess the accuracy of FCNN in identifying T2-hyperintense MS lesions.
  • To validate the robustness of the FCNN model across different imaging centers.

Main Methods:

  • A FCNN model was developed and trained using approximately 1000 multimodal magnetic resonance imaging (MRI) datasets from relapsing-remitting MS patients.
  • Input data included dual-echo, FLAIR, and T1-weighted MRI sequences.
  • Expert-validated segmentations served as ground truth, and results were cross-validated using a leave-one-center-out approach.

Main Results:

  • The FCNN achieved high Dice similarity coefficients for segmented tissues: 0.95 for white matter, 0.96 for gray matter, and 0.99 for cerebrospinal fluid.
  • The model demonstrated strong performance in segmenting T2 MS lesions with a Dice score of 0.82.
  • High correlations (R² > 0.92) were observed between DL-segmented and ground truth tissue volumes, with consistent cross-validation results across centers.

Conclusions:

  • Deep fully convolutional neural networks (FCNNs) offer a highly accurate and automated solution for segmenting brain tissues and MS lesions.
  • The FCNN model's performance is robust and consistent across different imaging centers, making it suitable for large-scale studies.
  • This DL-based approach has the potential to significantly improve the efficiency and reliability of MS lesion and tissue segmentation in clinical practice and research.