Adaptive Model Based Pulmonary Artery Segmentation in 3-D Chest CT

被引：3

作者：

Feuerstein, Marco ^{[1
]}

Kitasaka, Takayuki ^{[2
,3
]}

Mori, Kensaku ^{[1
,3
]}

机构：

[1] Nagoya Univ, Grad Sch Informat Sci, Nagoya, Aichi 4648601, Japan

[2] Aichi Inst Technol, Fac Informat Sci, Aichi, Japan

[3] Nagoya Univ, MEXT Innovat Ctr Prevent Med Engn, Nagoya, Aichi 4648601, Japan

来源：

MEDICAL IMAGING 2010: IMAGE PROCESSING | 2010年 / 7623卷

关键词：

Segmentation; Vascular Image Processing; Shape; VESSELS;

D O I：

10.1117/12.843750

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

The extraction and analysis of the pulmonary artery in computed tomography (CT) of the chest can be an important, but time-consuming step for the diagnosis and treatment of lung disease, in particular in non-contrast data, where the pulmonary artery has low contrast and frequently merges with adjacent tissue of similar intensity. We here present a new method for the automatic segmentation of the pulmonary artery based on an adaptive model, Hough and Euclidean distance transforms, and spline fitting, which works equally well on non-contrast and contrast enhanced data. An evaluation on 40 patient data sets and a comparison to manual segmentations in terms of Jaccard index, sensitivity, specificity, and minimum mean distance shows its overall robustness.

引用

页数：9

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