Abstract
The segmentation of tree-like tubular structures such as coronary arteries
and airways is an essential step for many 3D medical imaging applications.
Statistical tracking techniques for the extraction of elongated structures have
received considerable attention in recent years due to their robustness against
image noise and pathological changes. However, most tracking methods are
limited to a speci¿c application and do not support branching structures
ef¿ciently. In this work, we present a novel statistical tracking approach for
the extraction of different types of tubular structures with ringlike crosssections. Domain-speci¿c knowledge is learned from training data sets and integrated into the tracking process by simple adaption of parameters. In addition, an ef¿cient branching detection algorithm is presented. This approach was evaluated by extracting coronary arteries from 32 CTA data sets and distal
airways from 20 CT scans. These data sets were provided by the organizers
of the workshop ‘3D Segmentation in the Clinic: A Grand Challenge IICoronary Artery Tracking (CAT08)’ and ‘Extraction of Airways from CT
2009 (EXACT’09)’. On average, 81.5% overlap and 0.51 mm accuracy for the
tracking of coronary arteries were achieved. For the extraction of airway trees,
51.3% of the total tree length, 53.6% of the total number of branches and a
4.98% false positive rate were attained. In both experiments, our approach is
comparable to state-of-the-art methods.
and airways is an essential step for many 3D medical imaging applications.
Statistical tracking techniques for the extraction of elongated structures have
received considerable attention in recent years due to their robustness against
image noise and pathological changes. However, most tracking methods are
limited to a speci¿c application and do not support branching structures
ef¿ciently. In this work, we present a novel statistical tracking approach for
the extraction of different types of tubular structures with ringlike crosssections. Domain-speci¿c knowledge is learned from training data sets and integrated into the tracking process by simple adaption of parameters. In addition, an ef¿cient branching detection algorithm is presented. This approach was evaluated by extracting coronary arteries from 32 CTA data sets and distal
airways from 20 CT scans. These data sets were provided by the organizers
of the workshop ‘3D Segmentation in the Clinic: A Grand Challenge IICoronary Artery Tracking (CAT08)’ and ‘Extraction of Airways from CT
2009 (EXACT’09)’. On average, 81.5% overlap and 0.51 mm accuracy for the
tracking of coronary arteries were achieved. For the extraction of airway trees,
51.3% of the total tree length, 53.6% of the total number of branches and a
4.98% false positive rate were attained. In both experiments, our approach is
comparable to state-of-the-art methods.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Physics in Medicine and Biology |
| Vol/bind | 57 |
| Udgave nummer | 16 |
| Sider (fra-til) | 5325-5342 |
| Antal sider | 18 |
| ISSN | 0031-9155 |
| DOI | |
| Status | Udgivet - 21 aug. 2012 |