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Endoscopic Polyp Segmentation Using a Hybrid 2D/3D CNN

Puyal, JGB; Bhatia, KK; Brandao, P; Ahmad, OF; Toth, D; Kader, R; Lovat, L; ... Stoyanov, D; + view all (2020) Endoscopic Polyp Segmentation Using a Hybrid 2D/3D CNN. In: (Proceedings) MICCAI 2020. (pp. pp. 295-305). Springer: Cham, Switzerland. Green open access

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Abstract

Colonoscopy is the gold standard for early diagnosis and pre-emptive treatment of colorectal cancer by detecting and removing colonic polyps. Deep learning approaches to polyp detection have shown potential for enhancing polyp detection rates. However, the majority of these systems are developed and evaluated on static images from colonoscopies, whilst applied treatment is performed on a real-time video feed. Non-curated video data includes a high proportion of low-quality frames in comparison to selected images but also embeds temporal information that can be used for more stable predictions. To exploit this, a hybrid 2D/3D convolutional neural network architecture is presented. The network is used to improve polyp detection by encompassing spatial and temporal correlation of the predictions while preserving real-time detections. Extensive experiments show that the hybrid method outperforms a 2D baseline. The proposed architecture is validated on videos from 46 patients. The results show that real-world clinical implementations of automated polyp detection can benefit from the hybrid algorithm.

Type: Proceedings paper
Title: Endoscopic Polyp Segmentation Using a Hybrid 2D/3D CNN
Event: MICCAI 2020
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-59725-2_29
Publisher version: https://doi.org/10.1007/978-3-030-59725-2_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: Colonoscopy, Polyp detection, Computer aided diagnosis
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 Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
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 Computer 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/10114066
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