iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. By Guilherme Aresta, Colin Jacobs, Teresa Araújo, António Cunha, Isabel Ramos, Bram van Ginneken and Aurélio Campilho (December 2018)

Please cite http://arxiv.org/abs/1811.12789

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.

• Download the repository
• run GUI_guided_segmentation.py
• load one of the .npy files via "Browse"
• perform the inital segmentation via "Segment"
• press "Select points" to add 2 points to the nodule
• the segmentation will change accordingly