TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Zeineldin, Ramy
A1  - Pollok, Alex
A1  - Mangliers, Tim
A1  - Karar, Mohamed
A1  - Mathis-Ullrich, Franziska
A1  - Burgert, Oliver
T1  - Deep automatic segmentation of brain tumours in interventional ultrasound data
JF  - Current directions in biomedical engineering
N2  - 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.
KW  - brain tumour
KW  - deep learning
KW  - image-guided neurosurgery
KW  - iUS
KW  - segmentation
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-37522
SN  - 2364-5504
SS  - 2364-5504
U6  - https://doi.org/10.1515/cdbme-2022-0034
DO  - https://doi.org/10.1515/cdbme-2022-0034
VL  - 8
IS  - 1
SP  - 133
EP  - 137
S1  - 5
PB  - De Gruyter
CY  - Berlin
ER  -