Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging

Evan Fletcher, Charles DeCarli, Audrey P. Fan, Alexander Knaack

Research output: Contribution to journalArticlepeer-review

Abstract

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.

Original languageEnglish (US)
Article number683426
JournalFrontiers in Neuroscience
Volume15
DOIs
StatePublished - Jun 21 2021

Keywords

  • brain segmentation
  • convolutional neural network
  • deep learning
  • magnetic resonance imaging
  • medical image processing
  • medical imaging data ground truth

ASJC Scopus subject areas

  • Neuroscience(all)

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