2016
DOI: 10.1109/tbme.2016.2574816
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Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT

Abstract: Objective This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans. Methods Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images (i.e., atlases) were pairwise registered based on intensity information with six registration tools (FSL, ANTS-CC, ANTS-QUICK-MI, IRTK, NIFTYREG, and DEEDS). The Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the regis… Show more

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Cited by 142 publications
(94 citation statements)
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References 33 publications
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“…Comparison to State-of-the-Art: Several approaches have been evaluated in the last few years on the NIH dataset and a similar corpus of abdominal CT scans (the BCV challenge data described in [29]). Accuracies for pancreas segmentation without CNNs are often relatively low, e.g.…”
Section: Methodsmentioning
confidence: 99%
“…Comparison to State-of-the-Art: Several approaches have been evaluated in the last few years on the NIH dataset and a similar corpus of abdominal CT scans (the BCV challenge data described in [29]). Accuracies for pancreas segmentation without CNNs are often relatively low, e.g.…”
Section: Methodsmentioning
confidence: 99%
“…The reason is that the relatively low inter-subject variation in overall brain shape and structure locations allows good alignment. In contrast, abdominal structures exhibit high location and shape variability and are challenging for registration [20].…”
Section: A Multiorgan Segmentation Methodsmentioning
confidence: 99%
“…µ� , � = � ≠ �. In (4), p indicates position and shows gray-scale value. The bilateral kernel motivates nearby pixels with similar intensity to be in the same class while unilateral is used for smoothness and elimination of isolated regions.…”
Section: Post-processingmentioning
confidence: 99%