Name
Title | ||
|---|---|---|
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation. |
Abstract | ||
|---|---|---|
Data availability plays a critical role for the performance of deep learning systems. This challenge is especially acute within the medical image domain, particularly when pathologies are involved, due to two factors: (1) limited number of cases, and (2) large variations in location, scale, and appearance. In this work, we investigate whether augmenting a dataset with artificially generated lung nodules can improve the robustness of the progressive holistically nested network (P-HNN) model for pathological lung segmentation of CT scans. To achieve this goal, we develop a 3D generative adversarial network (GAN) that effectively learns lung nodule property distributions in 3D space. In order to embed the nodules within their background context, we condition the GAN based on a volume of interest whose central part containing the nodule has been erased. To further improve realism and blending with the background, we propose a novel multi-mask reconstruction loss. We train our method on over 1000 nodules from the LIDC dataset. Qualitative results demonstrate the effectiveness of our method compared to the state-of-art. We then use our GAN to generate simulated training images where nodules lie on the lung border, which are cases where the published P-HNN model struggles. Qualitative and quantitative results demonstrate that armed with these simulated images, the P-HNN model learns to better segment lung regions under these challenging situations. As a result, our system provides a promising means to help overcome the data paucity that commonly afflicts medical imaging. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00934-2_81 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Lung nodule,CT,GAN,Dataset bottleneck,Lung segmentation | Conference | 11071 |
ISSN | Citations | PageRank |
0302-9743 | 12 | 0.66 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dakai Jin | 1 | 53 | 11.67 |
| Ziyue Xu | 2 | 597 | 35.50 |
| Youbao Tang | 3 | 107 | 12.00 |
| Adam P. Harrison | 4 | 101 | 17.06 |
| Daniel J Mollura | 5 | 614 | 30.82 |