Abstract
Fiber tract segmentation is a prerequisite for the tract-based statistical analysis and plays a crucial role in understanding brain structure and function. The previous researches mainly consist of two steps: defining and computing the similarity features of fibers, and then adopting machine learning algorithm for clustering or classification. Among them, how to define similarity is the basic premise and assumption of the whole method, and determines its potential reliability and application. The similarity features defined by previous studies ranged from geometric to anatomical, and then to functional characteristics, accordingly, the resulting fiber tracts seem more and more meaningful, while their reliability declined. Therefore, here we still adopt geometric feature for fiber tract segmentation, and put forward a novel descriptor (FiberGeoMap) for representing fiber’s geometric feature, which can depict effectively the shape and position of fiber, and can be inputted into our revised Transformer encoder network, called as FiberGeoMap Learner, which can well fully leverage the fiber’s features. Experimental results showed that the FiberGeoMap combined with FiberGeoMap Learner can effectively express fiber’s geometric features, and differentiate the 103 various fiber tracts, furthermore, the common fiber tracts across individuals can be identified by this method, thus avoiding additional image registration in preprocessing. The comparative experiments demonstrated that the proposed method had better performance than the existing methods. The code and more details are openly available at https://github.com/Garand0o0/FiberTractSegmentation.
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The work was supported by the National Natural Science Foundation of China (NSFC61976131 and NSFC61936007).
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Wang, Z., Lv, Y., He, M., Ge, E., Qiang, N., Ge, B. (2022). Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_14
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DOI: https://doi.org/10.1007/978-3-031-16431-6_14
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