Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial
for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation.
In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for
measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200
tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated
using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can
provide complementary information regarding the quality of the segmentation results. The reproducibility was
measured by the variation of the volume measurements from 10 independent segmentations. The effect of
disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our
results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all
four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The
segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient
of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient
of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this
study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale
evaluation of segmentation techniques for other clinical applications.
In the diagnosis of coronary artery disease, 3D-multi-slice
computed tomography (MSCT) has recently become more and more
important. In this work, an anatomical-based method for the
segmentation of atherosclerotic coronary arteries in MSCT is
presented. This technique is able to bridge severe stenosis, image
artifacts or even full vessel occlusions. Different anatomical
structures (aorta, blood-pool of the heart chambers, coronary
arteries and their orifices) are detected successively to
incorporate anatomical knowledge into the algorithm. The coronary
arteries are segmented by a simulated wave propagation method to
be able to extract anatomically spatial relations from the result.
In order to bridge segmentation breaks caused by stenosis or image
artifacts, the spatial location, its anatomical relation and
vessel curvature-propagation are taken into account to span a
dynamic search space for vessel bridging and gap closing. This
allows the prevention of vessel misidentifications and improves
segmentation results significantly. The robustness of this method
is proven on representative medical data sets.
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