Accuracy
Emin, S., Rossi, E., Hedman, M., Giovenco, M., Villegas, F., & Onjukka, E. (2025). Performance of multi-vendor auto-segmentation models for thoracic organs at risk trained on a single dataset. Physica Medica, 137, 105089. https://doi.org/10.1016/j.ejmp.2025.105089
Kiljunen, T., Akram, S., Niemelä, J., Löyttyniemi, E., Seppälä, J., Heikkilä, J., Vuolukka, K., Kääriäinen, O.-S., Heikkilä, V.-P., Lehtiö, K., Nikkinen, J., Gershkevitsh, E., Borkvel, A., Adamson, M., Zolotuhhin, D., Kolk, K., Pang, E. P. P., Tuan, J. K. L., Master, Z., … Keyriläinen, J. (2020). A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study. Diagnostics, 10(11), 959. https://doi.org/10.3390/diagnostics10110959
Olsson, C.E., Suresh, R., Niemelä, J., Akram, S. U., & Valdman, A. (2022). Autosegmentation based on different-sized training datasets of consistently-curated volumes and impact on rectal contours in prostate cancer radiation therapy. Physics and Imaging in Radiation Oncology, 22, 67–72. https://doi.org/10.1016/j.phro.2022.04.007
Strolin, S., Santoro, M., Paolani, G., Ammendolia, I., Arcelli, A., Benini, A., Bisello, S., Cardano, R., Cavallini, L., Deraco, E., Donati, C. M., Galietta, E., Galuppi, A., Guido, A., Ferioli, M., Laghi, V., Medici, F., Ntreta, M., Razganiayeva, N., … Strigari, L. (2023). How smart is artificial intelligence in organs delineation? testing a CE and FDA-approved deep-learning tool using multiple expert contours delineated on planning CT images. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1089807
Turcas, A., Leucuta, D., Balan, C., Clementel, E., Gheara, C., Kacso, A., Kelly, S. M., Tanasa, D., Cernea, D., & Achimas-Cadariu, P. (2023). Deep-Learning Magnetic Resonance Imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution. Physics and Imaging in Radiation Oncology, 27, 100454. https://doi.org/10.1016/j.phro.2023.100454
Doolan, P. J., Charalambous, S., Roussakis, Y., Leczynski, A., Peratikou, M., Benjamin, M., Ferentinos, K., Strouthos, I., Zamboglou, C., & Karagiannis, E. (2023). A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1213068
Warren, S., Richmond, N., Wowk, A., Wilkinson, M., & Wright, K. (2023). Ai segmentation as a quality improvement tool in radiotherapy planning for breast cancer. IPEM-Translation, 6–8, 100020. https://doi.org/10.1016/j.ipemt.2023.100020
Melerowitz, L., Sreenivasa, S., Nachbar, M., Stsefanenka, A., Beck, M., Senger, C., Predescu, N., Ullah Akram, S., Budach, V., Zips, D., Heiland, M., Nahles, S., & Stromberger, C. (2024). Design and evaluation of a Deep Learning-based automatic segmentation of maxillary and mandibular substructures using a 3D U-Net. Clinical and Translational Radiation Oncology, 47, 100780. https://doi.org/10.1016/j.ctro.2024.100780
Meixner, E., Glogauer, B., Klüter, S., Wagner, F., Neugebauer, D., Hoeltgen, L., Dinges, L. A., Harrabi, S., Liermann, J., Vinsensia, M., Weykamp, F., Hoegen-Saßmannshausen, P., Debus, J., & Hörner-Rieber, J. (2024). Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation. Clinical and Translational Radiation Oncology, 49, 100855. https://doi.org/10.1016/j.ctro.2024.100855
Bordigoni, B., Trivellato, S., Pellegrini, R., Meregalli, S., Bonetto, E., Belmonte, M., Castellano, M., Panizza, D., Arcangeli, S., & De Ponti, E. (2024). Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of atlas-, machine learning-, and Deep Learning-based models. Physica Medica, 125, 104486. https://doi.org/10.1016/j.ejmp.2024.104486
Miura, H., Ishihara, S., Kenjo, M., Nakao, M., Ozawa, S., & Kagemoto, M. (2025). Evaluation of the accuracy of automated segmentation based on Deep Learning for Prostate Cancer Patients. Medical Dosimetry, 50(1), 91–95. https://doi.org/10.1016/j.meddos.2024.09.002
Meyer, C., Huger, S., Bruand, M., Leroy, T., Palisson, J., Rétif, P., Sarrade, T., Barateau, A., Renard, S., Jolnerovski, M., Demogeot, N., Marcel, J., Martz, N., Stefani, A., Sellami, S., Jacques, J., Agnoux, E., Gehin, W., Trampetti, I., … Faivre, J.-C. (2024). Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas. Radiation Oncology, 19(1). https://doi.org/10.1186/s13014-024-02554-y
Miura, H., Ishihara, S., Kenjo, M., Nakao, M., Ozawa, S., & Kagemoto, M. (2025b). Performance evaluation of MVision AI contour+ in Gastric malt lymphoma segmentation. Reports of Practical Oncology and Radiotherapy, 30(1), 122–125. https://doi.org/10.5603/rpor.104144
Pang, E. P., Tan, H. Q., Wang, F., Niemelä, J., Bolard, G., Ramadan, S., Kiljunen, T., Capala, M., Petit, S., Seppälä, J., Vuolukka, K., Kiitam, I., Zolotuhhin, D., Gershkevitsh, E., Lehtiö, K., Nikkinen, J., Keyriläinen, J., Mokka, M., & Chua, M. L. (2025). Multicentre evaluation of deep learning CT Autosegmentation of the head and neck region for radiotherapy. Npj Digital Medicine, 8(1). https://doi.org/10.1038/s41746-025-01624-z
