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Published on: July 14, 2020
Segmentation of brain tumour in 3D Intraoperative Ultrasound imaging
Erick Angel-Raya1, Claire Chalopin2, Juan Gabriel Avina-Cervantes1
1Engineering Division (DICIS), Department of Electronics Engineering, University of Guanajuato, Campus Irapuato-Salamanca, Salamanca, Mexico.
Background:
Intraoperative ultrasound (iUS), using a navigation system and preoperative magnetic resonance imaging (pMRI), supports the surgeon intraoperatively in identifying tumour margins. Therefore, visual tumour enhancement can be supported by efficient segmentation methods.
Methods:
A semi-automatic and two registration-based segmentation methods are evaluated to extract brain tumours from 3D-iUS data. The registration-based methods estimated the brain deformation after craniotomy based on pMRI and 3D-iUS data. Both approaches use the normalised gradient field and linear correlation of linear combinations metrics. Proposed methods were evaluated on 66 B-mode and contrast-mode 3D-iUS data with metastasis and glioblastoma.
Results:
The semi-automatic segmentation achieved superior results with dice similarity index (DSI) values between [85.34, 86.79]% and contour mean distance values between [1.05, 1.11] mm for both modalities and tumour classes.
Conclusions:
Better segmentation results were obtained for metastasis detection than glioblastoma, preferring 3D-intraoperative B-mode over 3D-intraoperative contrast-mode.
