Automatic detect lung node with deep learning in segmentation and imbalance data labeling

In this study, a novel method with the U-Net-based network architecture, 2D U-Net, is employed to segment the position of lung nodules, which are an early symptom of lung cancer and have a high probability of becoming a carcinoma, especially when a lung nodule is bigger than 15 [Formula: see text]. A serious problem of considering deep learning for all medical images is imbalanced labeling between foreground and background. The lung nodule is the foreground which accounts for a lower percentage in a whole image. The evaluation function adopted in this study is dice coefficient loss, which is usually used in image segmentation tasks. The proposed pre-processing method in this study is to use complementary labeling as the input in U-Net. With this method, the labeling is swapped. The no-nodule position is labeled. And the position of the nodule becomes non-labeled. The result shows that the proposal in this study is efficient in a small quantity of data. This method, complementary labeling could be used in a small data quantity scenario. With the use of ROI segmentation model in the data pre-processing, the results of lung nodule detection can be improved a lot as shown in the experiments.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:11

Enthalten in:

Scientific reports - 11(2021), 1 vom: 27. Mai, Seite 11174

Sprache:

Englisch

Beteiligte Personen:

Chiu, Ting-Wei [VerfasserIn]
Tsai, Yu-Lin [VerfasserIn]
Su, Shun-Feng [VerfasserIn]

Links:

Volltext

Themen:

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 23.11.2021

Date Revised 23.11.2021

published: Electronic

Citation Status MEDLINE

doi:

10.1038/s41598-021-90599-4

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM325943249