Normal appearance autoencoder for lung cancer detection and segmentation

Astaraki, Mehdi,PhD Student,1984-KTH,Medicinsk avbildning,Department of Oncology-Pathology, Karolinska Institutet Karolinska Universitetssjukhuset Stockholm Sweden (författare)

QC 20210622
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.

TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
Lung nodule segmentation
Anomaly detection
Convolutional variational autoencoder
Medical Technology
Medicinsk teknologi

Toma-Dasu, Iuliana (författare)
Smedby, Örjan,Professor,1956-KTH,Medicinsk avbildning(Swepub:kth)u1vc2uzb (författare)
Wang, Chunliang,1980-KTH,Medicinsk avbildning(Swepub:kth)u1tbkeej (författare)
KTHMedicinsk avbildning (creator_code:org_t)

Ingår i:International Conference on Medical Image Computing and Computer-Assisted InterventionCham : Springer Nature, s. 249-256

Av författaren/redakt...: Astaraki, Mehdi, ...; Toma-Dasu, Iulia ...; Smedby, Örjan, P ...; Wang, Chunliang, ...

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Medicinteknik; och Medicinsk bildbe ...

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