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DH-GAC: deep hierarchical context fusion network with modified geodesic active contour for multiple neurofibromatosis segmentation

  • S.I.: Efficient Artificial Intelligent Algorithms for Medical Image Analysis Based on High-Performance Computing
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Abstract

Delineating accurately and simultaneously all lesions is vital and challenging for computer-aided diagnosis for multiple neurofibromatosis (NF). However, existing CNN-based segmentation methods paid little attention to weak boundaries. Moreover, due to the intensity-inhomogeneous distribution of medical images, the ambiguous boundaries, and highly variable locations, sizes and shapes of the lesions, delineating multiple lesions simultaneously remains quite challenging. To address these challenges, we introduce a novel end-to-end segmentation framework of multiple NF, deep hierarchical geodesic active contour (DH-GAC). It leverages the elaborately designed deep hierarchical context fusion network (DH-CFN) to improve the generalization and robustness of DH-GAC, and the modified geodesic active contour (MGAC) to delineate precisely all lesions as much as possible. Specifically, it employs DH-CFN to predict specific parameter maps of each image for MGAC and feeds them into the energy function of MGAC to delineate NF lesions, which makes DH-GAC end-to-end trainable. Moreover, to improve the generalization of DH-GAC, we adopt two different settings to initialize the surface for DH-GAC. Experimental results demonstrate that DH-GAC not only improves the segmentation precision, but also overcomes the intrinsic drawback of classical geodesic active contour in boundary delineation.

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Data availability

The data that support the findings of this study are available from Harvard Medical School but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from the last author upon reasonable request and with permission of Harvard Medical School.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62072168) and Natural Science Foundation of Hunan Province (Grant No. 2021JJ30148).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Lushan Road, Changsha, 410082, Hunan, China

    Xiangqiong Wu, Guanghua Tan, Bin Pu & Mingxing Duan

  2. Harvard Medical School, Harvard University, 25 Shattuck Street, Boston, MA, 02115, USA

    Wenli Cai

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Contributions

Conceptualization was contributed by XW, GT, and WC; methodology was contributed by GT and WC; formal analysis and investigation were contributed by XW; technical guidance was contributed by GT and WC; writing-original draft preparation was contributed by XW; writing-review and editing was contributed by BP and MD; funding acquisition was contributed by GT and WC; visualization was contributed by XW and BP; and resources were contributed by MD and WC.

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Correspondence to Guanghua Tan.

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Wu, X., Tan, G., Pu, B. et al. DH-GAC: deep hierarchical context fusion network with modified geodesic active contour for multiple neurofibromatosis segmentation. Neural Comput & Applic 37, 7511–7526 (2025). https://doi.org/10.1007/s00521-022-07945-4

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