Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing

Benjamin Thyreau, Kazunori Sato, Hiroshi Fukuda, Yasuyuki Taki

研究成果: Article査読

41 被引用数 (Scopus)

抄録

The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model. Deep convolutional neural networks (ConvNets) have shown great success in recent years, due to their ability to learn meaningful features from a mass of training data. Our method relies on the following key novelties: (i) we use a wide and variable training set coming from multiple cohorts (ii) our training labels come in part from the output of the FreeSurfer algorithm, and (iii) we include synthetic data and use a powerful data augmentation scheme. Our method proves to be robust, and it has fast inference (<30s total per subject), with trained model available online (https://github.com/bthyreau/hippodeep). We depict illustrative results and show extensive qualitative and quantitative cohort-wide comparisons with FreeSurfer. Our work demonstrates that deep neural-network methods can easily encode, and even improve, existing anatomical knowledge, even when this knowledge exists in algorithmic form.

本文言語English
ページ(範囲)214-228
ページ数15
ジャーナルMedical Image Analysis
43
DOI
出版ステータスPublished - 2018 1月

ASJC Scopus subject areas

  • 放射線技術および超音波技術
  • 放射線学、核医学およびイメージング
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学
  • コンピュータ グラフィックスおよびコンピュータ支援設計

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