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Congress: ECR25
Poster Number: C-27006
Type: Poster: EPOS Radiologist (scientific)
Authorblock: M. Balaguer-Montero1, A. Marcos Morales1, M. Ligero2, D. Leiva1, L. M. Atlagich1, N. Staikoglou1, C. Zatse1, C. Monreal1, R. Perez Lopez1; 1Barcelona/ES, 2Dresden/DE
Disclosures:
Maria Balaguer-Montero: Nothing to disclose
AdriĆ  Marcos Morales: Nothing to disclose
Marta Ligero: Nothing to disclose
David Leiva: Nothing to disclose
Luz Maria Atlagich: Nothing to disclose
Nikolaos Staikoglou: Nothing to disclose
Christina Zatse: Nothing to disclose
Camilo Monreal: Nothing to disclose
Raquel Perez Lopez: Nothing to disclose
Keywords: Artificial Intelligence, Liver, Oncology, CT, Computer Applications-General, Observer performance, Segmentation, Cancer
References
  1. Eisenhauer, E.A., Therasse, P., Bogaerts, J., Schwartz, L.H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009). New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247. https://doi.org10.1016/j.ejca.2008.10.026
  2. Bilic, P., Christ, P., Li, H.B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G.E.H., Chartrand, G., et al. (2023). The Liver Tumor Segmentation Benchmark (LiTS). Med. Image Anal. 84, 102680. https://doi.org/10.1016/j.media.2022.102680
  3. Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., Ronneberger, O., Summers, R.M., et al. (2022). The Medical Segmentation Decathlon. Nat. Commun. 13, 4128. https://doi.org/10.1038/s41467-022-30695-9
  4. Simpson, A.L., Peoples, J., Creasy, J.M., Fichtinger, G., Gangai, N., Lasso, A., Keshava Murthy, K.N., Shia, J., D’Angelica, M.I., and Do, R.K.G. (2023). Preoperative CT and survival data for patients undergoing resection of Colorectal Liver Metastases (Colorectal-Liver-Metastases). (The Cancer Imaging Archive). https://doi.org/10.7937/QXK2-QG03
  5. Moawad, A.W., Fuentes, D., Morshid, A., Khalaf, A.M., Elmohr, M.M., Abusaif, A., Hazle, J.D., Kaseb, A.O., Hassan, M., Mahvash, A., et al. (2021). Multimodality annotated HCC cases with and without advanced imaging segmentation. (The Cancer Imaging Archive). https://doi.org/10.7937/TCIA.5FNA-0924
  6. Wasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D.T., Cyriac, J., Yang, S., et al. (2023). TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiol Artif Intell 5, e230024. https://doi.org/10.1148/ryai.230024
  7. Ye, J., Wang, H., Huang, Z., Deng, Z., Su, Y., Tu, C., Wu, Q., Yang, Y., Wei, M., Niu, J., et al. (2022). Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans. https://doi.org/10.48550/arXiv.2210.07490
  8. Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., and Maier-Hein, K.H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203–211. https://doi.org/10.1038/s41592-020-01008-z
  9. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. https://doi.org/10.48550/arXiv.2102.04306
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