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Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation
Karolinska Institutet, Karolinska Universitetssjukhuset, Sweden.ORCID iD: 0000-0002-7101-240X
2019 (English)In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: Proceedings / [ed] Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan, Cham: Springer, 2019, Vol. 6, p. 249-256Conference paper, Published paper (Refereed)
Abstract [en]

One of the major differences between medical doctor training and machine learning is that doctors are trained to recognize normal/healthy anatomy first. Knowing the healthy appearance of anatomy structures helps doctors to make better judgement when some abnormality shows up in an image. In this study, we propose a normal appearance autoencoder (NAA), that removes abnormalities from a diseased image. This autoencoder is semi-automatically trained using another partial convolutional in-paint network that is trained using healthy subjects only. The output of the autoencoder is then fed to a segmentation net in addition to the original input image, i.e. the latter gets both the diseased image and a simulated healthy image where the lesion is artificially removed. By getting access to knowledge of how the abnormal region is supposed to look, we hypothesized that the segmentation network could perform better than just being shown the original slice. We tested the proposed network on the LIDC-IDRI dataset for lung cancer detection and segmentation. The preliminary results show the NAA approach improved segmentation accuracy substantially in comparison with the conventional U-Net architecture.

Place, publisher, year, edition, pages
Cham: Springer, 2019. Vol. 6, p. 249-256
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11769
Keywords [en]
Lung nodule segmentation, Anomaly detection, Convolutional variational autoencoder
National Category
Cancer and Oncology
Identifiers
URN: urn:nbn:se:su:diva-175628DOI: 10.1007/978-3-030-32226-7_28ISBN: 978-3-030-32225-0 (print)ISBN: 978-3-030-32226-7 (electronic)OAI: oai:DiVA.org:su-175628DiVA, id: diva2:1368360
Conference
22nd International Conference, Shenzhen, China, October 13–17, 2019
Available from: 2019-11-06 Created: 2019-11-06 Last updated: 2019-11-07Bibliographically approved

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CiteExportLink to record
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  • apa
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