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

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

Author information

1
INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal. guilherme.m.aresta@inesctec.pt.
2
Faculty of Engineering of University of Porto, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal. guilherme.m.aresta@inesctec.pt.
3
Radboud University Medical Center, 6525, Nijmegen, The Netherlands.
4
INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal.
5
Faculty of Engineering of University of Porto, Rua Doutor Roberto Frias, 4200-465, Porto, Portugal.
6
University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801, Vila Real, Portugal.
7
Faculty of Medicine of University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal.

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.

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