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Performance divergence with data discrepancy: a review

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Abstract

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.

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  1. Qatar Science and Technology Park / Qatar Robotic Surgery Centre, Qatar Foundation, Doha, Qatar

    Sarada Prasad Dakua

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  1. Sarada Prasad Dakua
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Correspondence to Sarada Prasad Dakua.

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Dakua, S.P. Performance divergence with data discrepancy: a review. Artif Intell Rev 40, 429–455 (2013). https://doi.org/10.1007/s10462-011-9289-8

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