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.
@article{arxiv.1811.12789,
title = {iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network},
author = {Guilherme Aresta and Colin Jacobs and Teresa Araújo and António Cunha and Isabel Ramos and Bram van Ginneken and Aurélio Campilho},
journal= {arXiv preprint arXiv:1811.12789},
year = {2018}
}
Comments
Pre-print submitted to IEEE Transactions on Biomedical Engineering