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

arXiv: https://arxiv.org/abs/1811.12789 · HTML: https://mlsanity.com/paper/1811.12789/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images](https://mlsanity.com/paper/2107.00418/) (2021)
- [AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation](https://mlsanity.com/paper/2110.05144/) (2021)
- [Improved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations](https://mlsanity.com/paper/2102.10607/) (2021)

## Abstract (arXiv)

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.