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Deep residual networks for automatic segmentation of laparoscopic videos of the liver

Gibson, E; Robu, MR; Thompson, S; Edwards, P; Schneider, C; Gurusamy, K; Davidson, B; ... Clarkson, MJ; + view all (2017) Deep residual networks for automatic segmentation of laparoscopic videos of the liver. In: Webster, RJ and Fei, B, (eds.) Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling. (pp. 101351M1-101351M6). Society of Photo-Optical Instrumentation Engineers (SPIE): Bellingham, Washington, USA. Green open access

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Abstract

MOTIVATION: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. METHOD: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. RESULTS: The CNN yielded segmentations with Dice scores ≥0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. CONCLUSION: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance.

Type: Proceedings paper
Title: Deep residual networks for automatic segmentation of laparoscopic videos of the liver
Event: Medical Imaging 2017, 11 February 2017, Orlando, Florida, USA
ISBN-13: 9781510607156
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.2255975
Publisher version: http://dx.doi.org/10.1117/12.2255975
Language: English
Additional information: This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Segmentation, deep learning, laparoscopic video, liver resection, liver staging, minimally invasive surgery, image-guided interventions
UCL classification: UCL > Provost and Vice Provost Offices
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 Surgical Biotechnology
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/1563392
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