Ai segments tumors using images and reports without masks
RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports
Computer Vision and Pattern Recognition
Summary
Tumor outlines in scans are hard to get because drawing them takes a lot of work. The authors designed an AI system that learns to find tumors using reports and multiple images taken over time, instead of relying on these outlines. The AI first uses reports and scans from the same patient over time to create tumor masks. Then, it trains another AI to find tumors from a single scan alone. This method works well even on tumors without public data masks and can help detect multiple cancers.
What this means in practice
- •For medical image analysis teams: Train AI to segment tumors without needing manual tumor masks by using patients' reports and multiple scan times for guidance.
- •For radiology ai product teams: Develop tumor segmentation features that work on single scans without needing accompanying reports or prior longitudinal data at deployment.$Commercial implications: Enables creation of AI tools for hospitals that provide tumor outlines from routine scans alone, expanding market reach without extra annotation costs.
Authors
Pedro R. A. S. Bassi, 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, Zongwei Zhou
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