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Congress: ECR25
Poster Number: C-16040
Type: Poster: EPOS Radiologist (scientific)
DOI: 10.26044/ecr2025/C-16040
Authorblock: X. Bai, X. Zhang, J. Zhang, L. Chen, Q. Peng, E. Guo, G. Zhang, H. Sun, Z. Jin; Beijing/CN
Disclosures:
Xin Bai: Nothing to disclose
Xiaoxiao Zhang: Nothing to disclose
Jiahui Zhang: Nothing to disclose
Li Chen: Nothing to disclose
Qianyu Peng: Nothing to disclose
Erjia Guo: Nothing to disclose
Gumuyang Zhang: Nothing to disclose
Hao Sun: Nothing to disclose
Zhengyu Jin: Nothing to disclose
Keywords: Abdomen, Artificial Intelligence, Urinary Tract / Bladder, CT, Computer Applications-3D, Computer Applications-Detection, diagnosis, Diagnostic procedure, Cancer
References

1. Ahmed T M, Rowe S P, Fishman E K, Soyer P, Chu L C (2024) Three-dimensional CT cinematic rendering of adrenal masses: Role in tumor analysis and management. Diagnostic and interventional imaging 105 (1):5-14. doi:10.1016/j.diii.2023.09.004

2. Park S Y, Park B K, Park J J, Kim C K (2016) Differentiation of Adrenal Hyperplasia From Adenoma by Use of CT Densitometry and Percentage Washout. AJR Am J Roentgenol 206 (1):106-112. doi:10.2214/AJR.15.14558

3. Yuan H, Kang B, Sun K, Qin S, Ji C, Wang X (2023) CT-based radiomics nomogram for differentiation of adrenal hyperplasia from lipid-poor adenoma: an exploratory study. BMC medical imaging 23 (1):4. doi:10.1186/s12880-022-00951-x

4. Li J, Li H, Zhang Y et al. (2024) MCNet: A multi-level context-aware network for the segmentation of adrenal gland in CT images. Neural Networks 170:136-148. doi:10.1016/j.neunet.2023.11.028

5. Kim T M, Choi S J, Ko J Y et al. (2022) Fully automatic volume measurement of the adrenal gland on CT using deep learning to classify adrenal hyperplasia. European radiology. doi:10.1007/s00330-022-09347-5

6. Robinson-Weiss C, Patel J, Bizzo B C et al. (2022) Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology:220101. doi:10.1148/radiol.220101

7. Singh Y, Kelm Z S, Faghani S et al. (2023) Deep learning approach for differentiating indeterminate adrenal masses using CT imaging. Abdom Radiol (NY). doi:10.1007/s00261-023-03988-w

8. Sut S K, Koc M, Zorlu G et al. (2023) Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images. Journal of Digital Imaging 36 (3):879-892. doi:10.1007/s10278-022-00759-9

9. Luo G, Yang Q, Chen T, Zheng T, Xie W, Sun H (2021) An optimized two-stage cascaded deep neural network for adrenal segmentation on CT images. Comput Biol Med 136:104749. doi:10.1016/j.compbiomed.2021.104749
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