Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography

Itzik Avital, Ilya Nelkenbaum, Galia Tsarfaty, Eli Konen, Nahum Kiryati, Arnaldo Mayer

Research output: Contribution to journalArticlepeer-review

Abstract

White matter tractography mapping is an important tool for neuro-surgical planning and navigation. It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automation tools are strongly needed for the task. In this article, we propose and compare several multi-modal fully convolutional network architectures for segmentation of sROIs. Inspired by their manual segmentation practice, anatomical information from T1w maps is fused by the network with directionally encoded color (DEC) maps to compute the segmentation. Qualitative and quantitative validation was performed on image data from 75 real tumor resection candidates for the sROIs of the motor tract, the arcuate fasciculus, and optic radiation. Favorable comparison was also obtained with state-of-the-art methods for the tumor dataset as well as the ISMRM 2017 traCED challenge dataset. The proposed networks showed promising results, indicating they may significantly improve the efficiency of pre-surgical tractography mapping, without compromising its quality.

Original languageEnglish
Article number8906050
Pages (from-to)1655-1667
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number5
DOIs
StatePublished - May 2020

Keywords

  • Fully convolutional neural networks
  • brain
  • multi-modal segmentation
  • tractography

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