Abstract
Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, tumor segmentation masks are expensive, time-consuming, and unavailable for many tumor types in public data. Instead, hospitals have vast, readily available data that can guide segmentation: radiology reports, longitudinal images, and multi-phase images. We use this readily available data to substitute for tumor masks in training AI for tumor segmentation. To this end, we propose a new architecture, RT-Super. It has a teacher network, which analyzes the patient's longitudinal images and reports to create high-quality tumor masks. These masks train a student network, which sees a single image and no report. At inference, when longitudinal images and reports are unavailable, we use the student. RT-Super uses a new CNN-Transformer architecture and novel Consistency Losses that exploit tumor location consistency across longitudinal images. We train RT-Super to segment esophagus, uterus and spleen tumors, which have few or no public masks. Even without training masks, RT-Super can segment these tumors and surpass public AI models. Overall, we demonstrate that learning from longitudinal images, multi-phase images, and reports can overcome mask scarcity and advance multi-cancer detection and segmentation. Code: https://github.com/MrGiovanni/RT-Super
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Cite this article
APA 7
Bassi, P. R. A. S., Li, W., Gu, H., Chen, J., Zhou, X., Zhu, Z., Er, S., Hamamci, I. E., Menze, B. H., Akan, G. E., Wang, K., Yang, Y., Yuille, A. L., & Zhou, Z. (2026). RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports. https://omanscience.com/en/articles/rt-super-learning-tumor-segmentation-from-longitudinal-images-and-reports
MLA 9
Bassi, Pedro R. A. S., et al. "RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports." https://omanscience.com/en/articles/rt-super-learning-tumor-segmentation-from-longitudinal-images-and-reports.
Chicago (author–date)
Bassi, Pedro R. A. S., Wenxuan Li, Hanxue Gu, Jieneng Chen, Xinze Zhou, Zheren Zhu, Sezgin Er, Ibrahim E. Hamamci, Bjoern H. Menze, Gulhan E. Akan, Kang Wang, Yang Yang, Alan L. Yuille, and Zongwei Zhou. 2026. "RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports." https://omanscience.com/en/articles/rt-super-learning-tumor-segmentation-from-longitudinal-images-and-reports.
Harvard
Bassi, P. R. A. S., Li, W., Gu, H., Chen, J., Zhou, X., Zhu, Z., Er, S., Hamamci, I. E., Menze, B. H., Akan, G. E., Wang, K., Yang, Y., Yuille, A. L. and Zhou, Z. (2026) 'RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports', Available at: https://omanscience.com/en/articles/rt-super-learning-tumor-segmentation-from-longitudinal-images-and-reports.
Vancouver
Bassi PRAS, Li W, Gu H, Chen J, Zhou X, Zhu Z, et al. RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports. https://omanscience.com/en/articles/rt-super-learning-tumor-segmentation-from-longitudinal-images-and-reports
IEEE
P. R. A. S. Bassi, W. Li, H. Gu, J. Chen, X. Zhou, Z. Zhu, S. Er, I. E. Hamamci, B. H. Menze, G. E. Akan, K. Wang, Y. Yang, A. L. Yuille, and Z. Zhou, "RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports," https://omanscience.com/en/articles/rt-super-learning-tumor-segmentation-from-longitudinal-images-and-reports.