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Deep automatic segmentation of brain tumours in interventional ultrasound data

  • 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.

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Metadaten
Author of HS ReutlingenZeineldin, Ramy; Burgert, Oliver
URN:urn:nbn:de:bsz:rt2-opus4-37522
DOI:https://doi.org/10.1515/cdbme-2022-0034
ISSN:2364-5504
Erschienen in:Current directions in biomedical engineering
Publisher:De Gruyter
Place of publication:Berlin
Document Type:Article
Language:English
Year of Publication:2022
Tag:brain tumour; deep learning; iUS; image-guided neurosurgery; segmentation
Volume:8
Issue:1
Page Number:5
First Page:133
Last Page:137
DDC classes:570 Biowissenschaften, Biologie
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International