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An efficient and accurate method for robust inter-dataset brain extraction and comparisons with 9 other methods.

Philip Novosad1,2, D Louis Collins1,2,

  • 1McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

Human Brain Mapping
|July 5, 2018
PubMed
Summary

A novel multi-atlas segmentation method improves magnetic resonance neuroimaging brain extraction accuracy and robustness across diverse datasets. This new approach offers significant speed improvements, making brain extraction more efficient for research studies.

Keywords:
accuratebrain extractionefficienterror correctionfastmulti-atlas segmentationpatch-based label fusionrobustskull stripping

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Brain extraction is crucial for neuroimaging analysis but faces challenges due to anatomical variability and scanner differences.
  • Existing learning-based methods struggle with cross-dataset generalizability.
  • Robust and accurate brain extraction across diverse datasets remains a significant challenge.

Purpose of the Study:

  • To develop a patch-based multi-atlas segmentation method for accurate and robust brain extraction across different magnetic resonance neuroimaging datasets.
  • To evaluate the proposed method's performance against existing techniques in both inter-dataset and intra-dataset scenarios.
  • To assess the computational efficiency of the new method, especially when combined with error correction.

Main Methods:

  • Proposed a novel patch-based multi-atlas segmentation approach for brain extraction.
  • Utilized a diverse collection of labeled images from 5 different datasets for training and testing.
  • Compared the proposed method with 9 other brain extraction techniques, with and without machine learning-based error correction.
  • Validated performance on an independent multi-center dataset.

Main Results:

  • The proposed method achieved high accuracy in both inter-dataset (mean Dice 98.57%) and intra-dataset (mean Dice 99.02%) segmentation scenarios after error correction.
  • Demonstrated excellent volumetric correlation (0.994 inter-dataset, 0.998 intra-dataset) across datasets.
  • Combined with error correction, the method ran over 10 times faster than other top-performing methods in inter-dataset comparisons.
  • Validation on an independent multi-center dataset confirmed the method's excellent performance.

Conclusions:

  • The proposed patch-based multi-atlas segmentation method provides accurate, robust, and efficient brain extraction for magnetic resonance neuroimaging.
  • The method demonstrates superior performance across diverse datasets, addressing limitations of current learning-based approaches.
  • This technique offers a valuable tool for neuroimaging research, enhancing the reliability and speed of brain extraction.