Conference proceeding
Pulmonary Lobe Segmentation Using A Sequence of Convolutional Neural Networks For Marginal Learning
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Vol.2019-, pp.1207-1211
04/2019
DOI: 10.1109/ISBI.2019.8759212
Abstract
Segmentation of the pulmonary lobes in computed tomography images is an important precursor for characterizing and quantifying disease patterns, regional functional analysis, and determining treatment interventions. With the increasing resolution and quantity of scans produced in the clinic automatic and reliable lobar segmentation methods are essential for efficient workflows. In this work, a deep learning framework is proposed that utilizes convolutional neural networks for segmentation of fissures and lobes in computed tomography images. A novel pipeline is proposed that consists of a series of 3D convolutional neural networks to marginally learn the lobe segmentation. The method was evaluated extensively on a dataset of 1076 CT images from the COPDGene clinical trial, consisting of scans acquired multiple institutions using various scanners. Overall the method achieved median Dice coefficient of 0.993 and a median average symmetric surface distance of 0.138 mm across all lobes. The results show the method is robust to different inspiration levels, pathologies, and image quality.
Details
- Title: Subtitle
- Pulmonary Lobe Segmentation Using A Sequence of Convolutional Neural Networks For Marginal Learning
- Creators
- Sarah E Gerard - University of IowaJoseph M Reinhardt - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Vol.2019-, pp.1207-1211
- Publisher
- IEEE
- DOI
- 10.1109/ISBI.2019.8759212
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/2019
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984196976302771
Metrics
31 Record Views