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@article{Li:2018:10.1007/s11548-018-1806-7,
author = {Li, Y and Charalampaki, P and Liu, Y and Yang, G-Z and Giannarou, S},
doi = {10.1007/s11548-018-1806-7},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1187--1199},
title = {Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data},
url = {http://dx.doi.org/10.1007/s11548-018-1806-7},
volume = {13},
year = {2018}
}

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TY  - JOUR
AB  - Purpose: Probe-based confocal laser endomicroscopy (pCLE) enables in vivo, in situ tissue characterisation without changes in the surgical setting and simplifies the oncological surgical workflow. The potential of this technique in identifying residual cancer tissue and improving resection rates of brain tumours has been recently verified in pilot studies. The interpretation of endomicroscopic information is challenging, particularly for surgeons who do not themselves routinely review histopathology. Also, the diagnosis can be examiner-dependent, leading to considerable inter-observer variability. Therefore, automatic tissue characterisation with pCLE would support the surgeon in establishing diagnosis as well as guide robot-assisted intervention procedures.Methods: The aim of this work is to propose a deep learning-based framework for brain tissue characterisation for context aware diagnosis support in neurosurgical oncology. An efficient representation of the context information of pCLE data is presented by exploring state-of-the-art CNN models with different tuning configurations. A novel video classification framework based on the combination of convolutional layers with long-range temporal recursion has been proposed to estimate the probability of each tumour class. The video classification accuracy is compared for different network architectures and data representation and video segmentation methods.Results: We demonstrate the application of the proposed deep learning framework to classify Glioblastoma and Meningioma brain tumours based on endomicroscopic data. Results show significant improvement of our proposed image classification framework over state-of-the-art feature-based methods. The use of video data further improves the classification performance, achieving accuracy equal to 99.49%.Conclusions: This work demonstrates that deep learning can provide an efficient representation of pCLE data and accurately classify Glioblastoma and Meningioma tumours. Th
AU  - Li,Y
AU  - Charalampaki,P
AU  - Liu,Y
AU  - Yang,G-Z
AU  - Giannarou,S
DO  - 10.1007/s11548-018-1806-7
EP  - 1199
PY  - 2018///
SN  - 1861-6410
SP  - 1187
TI  - Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data
T2  - International Journal of Computer Assisted Radiology and Surgery
UR  - http://dx.doi.org/10.1007/s11548-018-1806-7
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000440294000006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://link.springer.com/article/10.1007/s11548-018-1806-7
UR  - http://hdl.handle.net/10044/1/62868
VL  - 13
ER  -

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