AAI_2025_Capstone_Chronicles_Combined

16

in a new hospital. BJR|Open , 6 (1), tzad008. https://doi.org/10.1093/bjro/tzad008 Kunkyab, T., Bahrami, Z., Zhang, H., Liu, Z., & Hyde, D. (2024). A deep learning ‐ based framework (Co ‐ ReTr) for auto ‐ segmentation of non ‐ small cell ‐ lung cancer in computed tomography images. Journal of Applied Clinical Medical Physics , 25 (3), e14297. https://doi.org/10.1002/acm2.14297 Qadir, H. A., Shin, Y., Solhusvik, J., Bergsland, J., Aabakken, L., & Balasingham, I. (2021). Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction. Medical Image Analysis , 68 , 101897. https://doi.org/10.1016/j.media.2020.10189 7 Rutkowski, T. (2014). The role of tumor volume in radiotherapy of patients with head and neck cancer. Radiation Oncology , 9 (1), 23. https://doi.org/10.1186/1748-717X-9-23 Sherer, M. V., Lin, D., Elguindi, S., Duke, S., Tan, L.-T., Cacicedo, J., Dahele, M., & Gillespie, E. F. (2021). Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiotherapy and Oncology , 160 , 185–191. https://doi.org/10.1016/j.radonc.2021.05.00 3 Stuppner, S., Waskiewicz, J., & Ruiu, A. (2020). Gross tumour volume comparison in oropharynx carcinomas using different intelligent imaging software. A retrospective analysis. Polish Journal of Radiology , 85 , 287–292. https://doi.org/10.5114/pjr.2020.96156

deep learning-based biomedical image segmentation. Nature Methods , 18 (2), 203–211. https://doi.org/10.1038/s41592-020-01008 z Jemaa, S., Fredrickson, J., Coimbra, A., Carano, R. A., El-Galaly, T. C. C., Knapp, A., Nielsen, T. G., Sahin, D., Bengtsson, T., & De Crespigny, A. (2019). A Fully Automated Measurement of Total Metabolic Tumor Burden in Diffuse Large B-Cell Lymphoma and Follicular Lymphoma. Blood , 134 (Supplement_1), 4666–4666. https://doi.org/10.1182/blood-2019-124793 Kashyap, M., Wang, X., Panjwani, N., Hasan, M., Zhang, Q., Huang, C., Bush, K., Chin, A., Vitzthum, L. K., Dong, P., Zaky, S., Loo, B. W., Diehn, M., Xing, L., Li, R., & Gensheimer, M. F. (2025). Automated Deep Learning–based Detection and Segmentation of Lung Tumors at CT Imaging. Radiology , 314 (1), e233029. https://doi.org/10.1148/radiol.233029 Kuker, R. A., Lehmkuhl, D., Kwon, D., Zhao, W., Lossos, I. S., Moskowitz, C. H., Alderuccio, J. P., & Yang, F. (2022). A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma. Cancers , 14 (21), 5221. https://doi.org/10.3390/cancers14215221 Kulkarni, C., Sherkhane, U., Jaiswar, V., Mithun, S., Mysore Siddu, D., Rangarajan, V., Dekker, A., Traverso, A., Jha, A., & Wee, L. (2023). Comparing the performance of a deep learning-based lung gross tumour volume segmentation algorithm before and after transfer learning

413