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A Multi-task Network for Anatomy Identification in Endoscopic Pituitary Surgery

Das, Adrito; Khan, Danyal; Williams, Simon; Hanrahan, John; Borg, Anouk; Dorward, neil; Bano, Sophia; ... Stoyanov, Danail; + view all (2023) A Multi-task Network for Anatomy Identification in Endoscopic Pituitary Surgery. In: Greenspan, Hayit and Madabhushi, Anant and Mousavi, Parvin and Salcudean, Septimiu and Duncan, James and Syeda-Mahmood, Tanveer and Taylor, Russell, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. (pp. 472-482). Springer: Cham, Switzerland. Green open access

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Abstract

Pituitary tumours are in an anatomically dense region of the body, and often distort or encase the surrounding critical structures. This, in combination with anatomical variations and limitations imposed by endoscope technology, makes intra-operative identification and protection of these structures challenging. Advances in machine learning have allowed for the opportunity to automatically identifying these anatomical structures within operative videos. However, to the best of the authors’ knowledge, this remains an unaddressed problem in the sellar phase of endoscopic pituitary surgery. In this paper, PAINet (Pituitary Anatomy Identification Network), a multi-task network capable of identifying the ten critical anatomical structures, is proposed. PAINet jointly learns: (1) the semantic segmentation of the two most prominent, largest, and frequently occurring structures (sella and clival recess); and (2) the centroid detection of the remaining eight less prominent, smaller, and less frequently occurring structures. PAINet utilises an EfficientNetB3 encoder and a U-Net++ decoder with a convolution layer for segmentation and pooling layer for detection. A dataset of 64-videos (635 images) were recorded, and annotated for anatomical structures through multi-round expert consensus. Implementing 5-fold cross-validation, PAINet achieved 66.1% and 54.1% IoU for sella and clival recess semantic segmentation respectively, and 53.2% MPCK-20% for centroid detection of the remaining eight structures, improving on single-task performances. This therefore demonstrates automated identification of anatomical critical structures in the sellar phase of endoscopic pituitary surgery is possible.

Type: Book chapter
Title: A Multi-task Network for Anatomy Identification in Endoscopic Pituitary Surgery
ISBN-13: 978-3-031-43995-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-031-43996-4_45
Publisher version: https://doi.org/10.1007/978-3-031-43996-4_45
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: minimally invasive surgery, semantic segmentation, surgical AI, surgical vision
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
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 Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
URI: https://discovery.ucl.ac.uk/id/eprint/10194451
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