Accurate automatic segmentation and measurement of aortic aneurysm in CT angiography
Details
Serval ID
serval:BIB_9643E071E50C
Type
Inproceedings: an article in a conference proceedings.
Publication sub-type
Abstract (Abstract): shot summary in a article that contain essentials elements presented during a scientific conference, lecture or from a poster.
Collection
Publications
Institution
Title
Accurate automatic segmentation and measurement of aortic aneurysm in CT angiography
Title of the conference
RSNA 2007, Radiological Society of North America, 93rd Scientific Assembly and Annual Meeting
Address
Chicago, Illinois, November 25-30, 2007
Publication state
Published
Issued date
2007
Language
english
Abstract
PURPOSE: To evaluate the performance of a new Computer-Aided Detection (CAD) system for AUTOMATIC segmentation and measurement of Aortic Aneurysm (AA).
METHOD AND MATERIALS: Fifty CT Angiography (CTA) data, from which 30 indicated abdominal AA, were used to develop and evaluate the CAD system. Three Rdaiologists manually identified and segmented all AA areas independently using in-house software. For each of the three manual segmentations, an "average" AA region was obtained by taking the overlapping part of the three manual segmentations and the result were used as our "gold standard" for development and evaluation of the AUTOMATIC method. The proposed method first AUTOMATICally identifies and segments the aorta lumen. Once the Aorta and its centreline was found, a series of images perpendicular to its centerline was reconstructed to measure the diameters of aorta. A method was then developed to examine all objects attached to the segmented aorta and whose characteristics lie in a mumber of pre-defined criteria set by incorporating prior understanding of the normal expected variation of aorta, such as its diameters. These regions were considered as candidates for AA regions. The identified AA regions were used to initialise a deformable model-based method used to segment and quantify Thrombus area. The accuracy of the algorithm was validated by comparing the segmentation result against the average "manual segmentation" of three radiologists. Two metrics were calculated to assess the accuracy of the algorithm: the overlapping ratio (OR) and the coverage ratio (CR)
RESULTS: The average values for OR and CR was 0.95 (1 meaning 100% overlap), indicating that the outline segmentation of AA was very close to that defined manually by the radiologists.
CONCLUSION: The manual identification of the enlarged portions of the aorta on a number of cross-sectional images is extremely tedious, time-consuming and subjective process. By using the Computer Aided Detection more ACCURATE, cost effective plus faster result can be achieved.
CLINICAL RELEVANCE/APPLICATION: The progressive growth of AA may eventually cause rupture if not diagnosed or treated.
METHOD AND MATERIALS: Fifty CT Angiography (CTA) data, from which 30 indicated abdominal AA, were used to develop and evaluate the CAD system. Three Rdaiologists manually identified and segmented all AA areas independently using in-house software. For each of the three manual segmentations, an "average" AA region was obtained by taking the overlapping part of the three manual segmentations and the result were used as our "gold standard" for development and evaluation of the AUTOMATIC method. The proposed method first AUTOMATICally identifies and segments the aorta lumen. Once the Aorta and its centreline was found, a series of images perpendicular to its centerline was reconstructed to measure the diameters of aorta. A method was then developed to examine all objects attached to the segmented aorta and whose characteristics lie in a mumber of pre-defined criteria set by incorporating prior understanding of the normal expected variation of aorta, such as its diameters. These regions were considered as candidates for AA regions. The identified AA regions were used to initialise a deformable model-based method used to segment and quantify Thrombus area. The accuracy of the algorithm was validated by comparing the segmentation result against the average "manual segmentation" of three radiologists. Two metrics were calculated to assess the accuracy of the algorithm: the overlapping ratio (OR) and the coverage ratio (CR)
RESULTS: The average values for OR and CR was 0.95 (1 meaning 100% overlap), indicating that the outline segmentation of AA was very close to that defined manually by the radiologists.
CONCLUSION: The manual identification of the enlarged portions of the aorta on a number of cross-sectional images is extremely tedious, time-consuming and subjective process. By using the Computer Aided Detection more ACCURATE, cost effective plus faster result can be achieved.
CLINICAL RELEVANCE/APPLICATION: The progressive growth of AA may eventually cause rupture if not diagnosed or treated.
Create date
09/04/2008 17:17
Last modification date
20/08/2019 15:58
Usage data
