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Performance divergence with data discrepancy: a review
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PRE
AI
DOI：10.1007/s10462-011-9289-8.png)
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Abstract
En 中文
With rapid increase in disease variety, the role of image segmentation has been crucial in image guided surgery. Despite having a lot of existing methods, the robustness of an algorithm remains a concern with respect to the input image variety. This paper presents a state of art segmentation algorithms of MICCAI Grand Challenge and Conference 2007, 2008 and 2009. These algorithms are reported to have tested on real datasets used in MICCAI Grand Challenge 2007, 2008 and 2009. Due to the page constraint, selected papers based on some criteria are included in this review. In this work, we have implemented and evaluated all these methods on a particular data. The objective of this paper is to exhibit the divergence in performance if the input data is varied.
Keywords:
Segmentation
CT
MR
US
PET
Journal
IF:
13.9
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6.1K
Citations:
1.9W
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