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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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