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Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation
KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.ORCID-id: 0000-0001-5125-4682
KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.ORCID-id: 0000-0002-7750-1917
KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem.ORCID-id: 0000-0002-0442-3524
2019 (Engelska)Ingår i: 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Springer, 2019, Vol. 11769, s. 249-256Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Springer, 2019. Vol. 11769, s. 249-256
Serie
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11769
Nyckelord [en]
Anomaly detection, Convolutional variational autoencoder, Lung nodule segmentation
Nationell ämneskategori
Medicinteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-266080DOI: 10.1007/978-3-030-32226-7_28Scopus ID: 2-s2.0-85075827836ISBN: 9783030322250 (tryckt)OAI: oai:DiVA.org:kth-266080DiVA, id: diva2:1381122
Konferens
22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019; Shenzhen; China; 13 October 2019 through 17 October 2019
Anmärkning

QC 20191220

Tillgänglig från: 2019-12-20 Skapad: 2019-12-20 Senast uppdaterad: 2019-12-20Bibliografiskt granskad

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Astaraki, MehdiSmedby, ÖrjanWang, Chunliang

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