Pawelczyk, K;
Kawulok, M;
Nalepa, J;
Hayball, MP;
McQuaid, SJ;
Prakash, V;
Ganeshan, B;
(2017)
Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep Learning.
In: Battiato, S and Gallo, G and Schettini, R and Stanco, F, (eds.)
Proceedings of the 19th International Conference of Image Analysis and Processing – ICIAP 2017.
(pp. pp. 310-320).
Springer Nature: Cham, Switzerland.
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Abstract
Automatic detection of lung lesions from computed tomography (CT) and positron emission tomography (PET) is an important task in lung cancer diagnosis. While CT scans make it possible to retrieve structural information, PET images reveal the functional aspects of the tissue, hence combined PET/CT imagery allows for detecting metabolically active lesions. In this paper, we explore how to exploit deep convolutional neural networks to identify the active tumour tissue exclusively from CT scans, which, to the best of our knowledge, has not been attempted yet. Our experimental results are very encouraging and they clearly indicate the possibility of detecting lesions with high glucose uptake, which could increase the utility of CT in lung cancer diagnosis.
| Type: | Proceedings paper |
|---|---|
| Title: | Towards Detecting High-Uptake Lesions from Lung CT Scans Using Deep Learning |
| Event: | 19th International Conference of Image Analysis and Processing – ICIAP 2017 |
| Location: | Catania, ITALY |
| Dates: | 11th-15th September 2017 |
| ISBN-13: | 978-3-319-68547-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-319-68548-9_29 |
| Publisher version: | https://doi.org/10.1007/978-3-319-68548-9_29 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | PET/CT imaging, Lesion detection, Deep neural networks |
| 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 Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10058534 |
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