Abstract #2136

Impact of Fiber Segmentation Methods on Fiber Quantification Reliability

Lingyu Li^1,2, QiQi Tong³, Silei Zhu⁴, Chenxi Lu^2,5, and Hongjian He^2,6,7

¹Polytechnic Institute, Zhejiang University, Hangzhou, China, ²Center for Brain Imaging Science and Technology, Zhejiang University, Hangzhou, China, ³Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China, ⁴Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, London, United Kingdom, ⁵College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China, ⁶School of Physics, Zhejiang University, Hangzhou, China, ⁷State Key Laboratory of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China

Synopsis

Keywords: Data Processing, Data Analysis, fiber quantification, tract segmentation, reliability

Motivation: The reliability of fiber quantification depends on the quality of the fiber tract reconstruction, which is influenced by the method used for fiber segmentation.

Goal(s): Our objective was to assess the reliability of different fiber segmentation methods and determine the most suitable strategy for the specific bundle.

Approach: We compared the distributions of intra-class coefficient (ICC) for different measurements and tracts across three widely used fiber segmentation methods. This analysis was conducted using the traveling subject dataset. The results are presented along with examples, followed by a discussion of the underlying reasons.

Results: We summarized the advantageous strategies for main tracts.

Impact: This strategy aims to enhance the stability of study results by facilitating the selection of more efficient segmentation methods for conducting fiber-specific quantification in the future.

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