Paper Info
Title
Neural-network-based automatic segmentation of cerebral ultrasound images for improving image-guided neurosurgery.
Abstract
Segmentation of anatomical structures in intraoperative ultrasound (iUS) images during image-guided interventions is challenging. Anatomical variances and the uniqueness of each procedure impede robust automatic image analysis. In addition, ultrasound image acquisition itself, especially acquired freehand by multiple physicians, is subject to major variability. In this paper we present a robust and fully automatic neural-network-based segmentation of central structures of the brain on B-mode ultrasound images. For our study we used iUS data sets from 18 patients, containing sweeps before, during, and after tumor resection, acquired at the University Hospital Essen, Germany. Different, machine learning approaches are compared and discussed in order to achieve results of highest quality without overfitting. We evaluate our results on the same data sets as in a previous publication in which the segmentations were used to improve iUS and preoperative Mill registration. Despite the smaller amount of data compared to other studies, we could efficiently train a U-net model for our purpose. Segmentations for this demanding task were performed with an average Dice coefficient of 0.88 and an average Hausdorff distance of 5.21 mm. Compared with a prior method for which a Random Forest, classifier was trained with handcrafted features, the Dice coefficient could be increased by 0.14 and the Hausdorff distance is reduced by 7 mm.
Year
DOI
Venue
2019
10.1117/12.2512289
Proceedings of SPIE
Keywords
DocType
Volume
Neurosurgical procedures,Deep Learning,Ultrasound guidance
Conference
10951
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Jennifer Nitsch100.34
Jan Klein29510.94
Jan Hendrik Moltz3808.85
Dorothea Miller401.01
Ulrich Sure511.72
Ron Kikinis667231071.86
Hans Meine7386.05