Journal
SCIENTIFIC REPORTS
Volume 9, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-019-48004-8
Keywords
-
Categories
Funding
- FCT [SFRH/BD/120435/2016, SFRH/BD/122365/2016]
- ERDF - European Regional Development Fund through Operational Programme for Competitiveness - COMPETE 2020 Programme [NLST-375]
- National Fundus through the Portuguese funding agency, FCT - Fundacao para a Ciencia e Tecnologia [POCI-01-0145-FEDER-016673]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available