TY - JOUR

T1 - Vessel-guided airway tree segmentation

T2 - a voxel classification approach

AU - Lo, Pechin Chien Pau

AU - Sporring, Jon

AU - Ashraf, Haseem

AU - Pedersen, Jesper Johannes Holst

AU - de Bruijne, Marleen

PY - 2010

Y1 - 2010

N2 - This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between airway and non-airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, airway tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the airway tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the airway appearance model produces more complete airway segmentations, leading to on average 20% longer trees, and 50% less leakage. When combining the airway appearance model with vessel orientation similarity, the improvement is even more significant than only using the airway appearance model, with on average 7% increase in the total length of branches extracted correctly.

AB - This paper presents a method for airway tree segmentation that uses a combination of a trained airway appearance model, vessel and airway orientation information, and region growing. We propose a voxel classification approach for the appearance model, which uses a classifier that is trained to differentiate between airway and non-airway voxels. This is in contrast to previous works that use either intensity alone or hand crafted models of airway appearance. We show that the appearance model can be trained with a set of easily acquired, incomplete, airway tree segmentations. A vessel orientation similarity measure is introduced, which indicates how similar the orientation of an airway candidate is to the orientation of the neighboring vessel. We use this vessel orientation similarity measure to overcome regions in the airway tree that have a low response from the appearance model. The proposed method is evaluated on 250 low dose computed tomography images from a lung cancer screening trial. Our experiments showed that applying the region growing algorithm on the airway appearance model produces more complete airway segmentations, leading to on average 20% longer trees, and 50% less leakage. When combining the airway appearance model with vessel orientation similarity, the improvement is even more significant than only using the airway appearance model, with on average 7% increase in the total length of branches extracted correctly.

U2 - 10.1016/j.media.2010.03.004

DO - 10.1016/j.media.2010.03.004

M3 - Journal article

C2 - 20395163

VL - 14

SP - 527

EP - 538

JO - Medical Image Analysis

JF - Medical Image Analysis

SN - 1361-8415

IS - 4

ER -