Normal Appearance Autoencoder for Lung Cancer Detection and Segmentation

  • Mehdi AstarakiEmail author
  • Iuliana Toma-Dasu
  • Örjan Smedby
  • Chunliang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


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.


Lung nodule segmentation Anomaly detection Convolutional variational autoencoder 



This study was supported by the Swedish Childhood Cancer Foundation (grant no. MT2016-0016) and the Swedish innovation agency Vinnova (grant no. 2017-01247).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehdi Astaraki
    • 1
    • 2
    Email author
  • Iuliana Toma-Dasu
    • 2
  • Örjan Smedby
    • 1
  • Chunliang Wang
    • 1
  1. 1.Department of Biomedical Engineering and Health SystemsKTH Royal Institute of TechnologyHuddingeSweden
  2. 2.Department of Oncology-Pathology, Karolinska InstitutetKarolinska UniversitetssjukhusetStockholmSweden

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