Abstract
Craniopharyngioma (CP) is one of the most common intracranial tumors located in the sellar region and its surroundings, which often leads to visual acuity, visual field disorders, and pituitary hypothalamus dysfunction. Segmentation of CP is an essential prerequisite in the diagnosis, screening, and treatment. Also, It’s a challenging task due to the indistinguishable borders, the small tumor size, and high diversity in size, shape, and texture. In this work, a novel automatic coarse-to-fine CP segmentation network is proposed, consisting of two stages: the coarse segmentation stage and the refinement stage. During the first stage, the Coarse Segmentation Guided Module (CSGM) is proposed to generate rough segmentation results and exclude the interference of background regions. During the refinement stage, the Local Feature Aggregation (LFA) module is proposed to solve the boundary ambiguity problem. It can encode the fine-grained information and adaptively explore the dependencies between a local spatial neighborhood. To validate the effectiveness of our model, a realistic CP dataset was constructed and a 4.26% dice score promotion is achieved compared to the baseline.
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Acknowledgements
This work was supported by the National Natural Science Fund for Distinguished Young Scholar under Grants No. 62025601.
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Yu, Y., Zhang, L., Shu, X., Wang, Z., Chen, C., Xu, J. (2022). A Coarse-to-Fine Network for Craniopharyngioma Segmentation. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds) Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_10
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