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Knowledge-driven deep neural network models for brain tumour segmentation

Colecchia, F; Ruffle, JK; Pombo, GC; Gray, R; Hyare, H; Nachev, P; (2020) Knowledge-driven deep neural network models for brain tumour segmentation. In: Journal of Physics: Conference Series. IOP Green open access

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Abstract

Image segmentation is a computer vision task aiming to establish a probabilistic mapping between individual pixels (2D) or voxels (3D) in an input image and a set of predefined semantic categories with reference to domain-specific knowledge. When applied to medical images, e.g. Magnetic Resonance Imaging (MRI), it allows delineation between healthy and abnormal tissue. Despite challenges due to lesion morphological heterogeneity, segmentation of brain tumours has the potential to streamline otherwise time-consuming manual annotation. Whereas brain tumour segmentation has continually advanced incorporating innovative deep learning methods, heuristics normally employed by radiologists have often been neglected. The focus of nearly all tumour segmentation articles thus far on 3D isotropic research-grade scans has also led to results of unknown generalisability to hospital-quality data. In order to address these gaps, this study has coalesced modern deep learning methods and clinical-driven priors into an optimised segmentation pipeline evaluated on clinical data at a large neurology and neurosurgery tertiary centre.

Type: Proceedings paper
Title: Knowledge-driven deep neural network models for brain tumour segmentation
Event: Micro-Mini & Nano Dosimetry and Innovative Technologies in Radiation Oncology - MMND ITRO 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/1742-6596/1662/1/012010
Publisher version: https://doi.org/10.1088/1742-6596/1662/1/012010
Language: English
Additional information: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10116239
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