Name
Title | ||
|---|---|---|
Pathological Pulmonary Lobe Segmentation From Ct Images Using Progressive Holistically Nested Neural Networks And Random Walker |
Abstract | ||
|---|---|---|
Automatic pathological pulmonary lobe segmentation(PPLS) enables regional analyses of lung disease, a clinically important capability. Due to often incomplete lobe boundaries, PPLS is difficult even for experts, and most prior art requires inference from contextual information. To address this, we propose a novel PPLS method that couples deep learning with the random walker (RW) algorithm. We first employ the recent progressive holistically-nested network (P-HNN) model to identify potential lobar boundaries, then generate final segmentations using a RW that is seeded and weighted by the P-HNN output. We are the first to apply deep learning to PPLS. The advantages are independence from prior airway/ vessel segmentations, increased robustness in diseased lungs, and methodological simplicity that does not sacrifice accuracy. Our method posts a high mean Jaccard score of 0.888 +/- 0.164 on a held-out set of 154 CT scans from lung-disease patients, while also significantly (p<0.001) outperforming a state-of-the-art method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67558-9_23 | DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT |
Keywords | DocType | Volume |
Lung lobe segmentation, CT, Holistically nested neural network, Fissure, Random walker | Journal | 10553 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin George | 1 | 19 | 1.42 |
| Adam P. Harrison | 2 | 101 | 17.06 |
| Dakai Jin | 3 | 53 | 11.67 |
| Ziyue Xu | 4 | 597 | 35.50 |
| Daniel J Mollura | 5 | 614 | 30.82 |