Abstract
The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.
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Acknowledgement
This work was supported by Project funded by China Postdoctoral Science Foundation (2019M652156), National Science Foundation Program of China (61601029), Zhejiang Provincial Natural Science Foundation (LZ19F010001).
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Su, P. et al. (2019). Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_2
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DOI: https://doi.org/10.1007/978-3-030-32251-9_2
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