iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network

Sci Rep. 2019 Aug 12;9(1):11591. doi: 10.1038/s41598-019-48004-8.

Abstract

We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule's boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system's loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Automation*
  • Early Detection of Cancer
  • Humans
  • Lung Diseases / diagnostic imaging*
  • Lung Diseases / pathology
  • Neural Networks, Computer