Abstract
The intestine is an essential digestive organ that can cause serious health problems once diseased. This paper proposes a method for intestine segmentation to intestine obstruction diagnosis assistance called multi-dimensional U-Net (M U-Net). We employ two encoders to extract features from two-dimensional (2D) CT slices and three-dimensional (3D) CT patches. These two encoders collaborate to enhance the segmentation accuracy of the model. Additionally, we incorporate deep supervision with the M U-Net to reduce the limitation of training with sparse label data sets. The experimental results demonstrated that the Dice of the proposed method was 73.22%, the recall was 79.89%, and the precision was 70.61%.
Q. An—Contributing author.
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Acknowledgments
Thanks for the help and advice from Mori laboratory. A part of this research was supported by Hori Sciences and Arts Foundation, MEXT/JSPS KAKENHI (17H00867, 22H03203), the JSPS Bilateral International Collaboration Grants, and the JST CREST (JPMJCR20D5). And this work was also financially supported by the JST SPRING, Grant Number JPMJSP2125.
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An, Q. et al. (2024). M U-Net: Intestine Segmentation Using Multi-dimensional Features for Ileus Diagnosis Assistance. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2023. Lecture Notes in Computer Science, vol 14313. Springer, Cham. https://doi.org/10.1007/978-3-031-47076-9_14
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