Automatic Detection of Pulmonary Embolism in CTA Images
article
Abstract—Pulmonary embolism (PE) is a common life-threatening
disorder for which an early diagnosis is desirable. We propose
a new system for the automatic detection of PE in contrast-enhanced
CT images. The system consists of candidate detection, feature
computation and classification. Candidate detection focusses
on the inclusion of PE—even complete occlusions—and the exclusion
of false detections, such as tissue and parenchymal diseases.
Feature computation does not only focus on the intensity, shape and
size of an embolus, but also on locations and the shape of the pulmonary
vascular tree. Several classifiers have been tested and the
results show that the performance is optimized by using a bagged
tree classifier with two features based on the shape of a blood vessel
and the distance to the vessel boundary. The system was trained on
38 CT data sets. Evaluation on 19 other data sets showed that the
system generalizes well. The sensitivity of our system on the evaluation
data is 63% at 4.9 false positives per data set, which allowed
the radiologist to improve the number of detected PE by 22%.
disorder for which an early diagnosis is desirable. We propose
a new system for the automatic detection of PE in contrast-enhanced
CT images. The system consists of candidate detection, feature
computation and classification. Candidate detection focusses
on the inclusion of PE—even complete occlusions—and the exclusion
of false detections, such as tissue and parenchymal diseases.
Feature computation does not only focus on the intensity, shape and
size of an embolus, but also on locations and the shape of the pulmonary
vascular tree. Several classifiers have been tested and the
results show that the performance is optimized by using a bagged
tree classifier with two features based on the shape of a blood vessel
and the distance to the vessel boundary. The system was trained on
38 CT data sets. Evaluation on 19 other data sets showed that the
system generalizes well. The sensitivity of our system on the evaluation
data is 63% at 4.9 false positives per data set, which allowed
the radiologist to improve the number of detected PE by 22%.
TNO Identifier
160515
Source
IEEE Transactions on Medical Imaging, 28(August), pp. 1223-1230.
Pages
1223-1230
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