Deep automatic segmentation of brain tumours in interventional ultrasound data

Zeineldin RA, Pollok A, Mangliers T, Karar ME, Mathis-Ullrich F, Burgert O (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Current Directions in Biomedical Engineering De Gruyter

Book Volume: 8

Pages Range: 133-137

Journal Issue: 1

DOI: 10.1515/cdbme-2022-0034

Abstract

Intraoperative imaging can assist neurosurgeons to define brain tumours and other surrounding brain structures. Interventional ultrasound (iUS) is a convenient modality with fast scan times. However, iUS data may suffer from noise and artefacts which limit their interpretation during brain surgery. In this work, we use two deep learning networks, namely UNet and TransUNet, to make automatic and accurate segmentation of the brain tumour in iUS data. Experiments were conducted on a dataset of 27 iUS volumes. The outcomes show that using a transformer with UNet is advantageous providing an efficient segmentation modelling long-range dependencies between each iUS image. In particular, the enhanced TransUNet was able to predict cavity segmentation in iUS data with an inference rate of more than 125 FPS. These promising results suggest that deep learning networks can be successfully deployed to assist neurosurgeons in the operating room.

Authors with CRIS profile

Franziska Mathis-Ullrich

Involved external institutions

Karlsruhe Institute of Technology (KIT) DE Germany (DE) Hochschule Reutlingen DE Germany (DE)

How to cite

APA:

Zeineldin, R.A., Pollok, A., Mangliers, T., Karar, M.E., Mathis-Ullrich, F., & Burgert, O. (2022). Deep automatic segmentation of brain tumours in interventional ultrasound data. Current Directions in Biomedical Engineering, 8(1), 133-137. https://doi.org/10.1515/cdbme-2022-0034

MLA:

Zeineldin, Ramy A., et al. "Deep automatic segmentation of brain tumours in interventional ultrasound data." Current Directions in Biomedical Engineering 8.1 (2022): 133-137.

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