From Pixel to Cancer: Cellular Automata in Computed Tomography
<|reference_start|>From Pixel to Cancer: Cellular Automata in Computed Tomography: AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in medical images, which can greatly diversify the data and annotations for AI training. However, current tumor synthesis approaches are not applicable across different organs due to their need for specific expertise and design. This paper establishes a set of generic rules to simulate tumor development. Each cell (pixel) is initially assigned a state between zero and ten to represent the tumor population, and a tumor can be developed based on three rules to describe the process of growth, invasion, and death. We apply these three generic rules to simulate tumor development--from pixel to cancer--using cellular automata. We then integrate the tumor state into the original computed tomography (CT) images to generate synthetic tumors across different organs. This tumor synthesis approach allows for sampling tumors at multiple stages and analyzing tumor-organ interaction. Clinically, a reader study involving three expert radiologists reveals that the synthetic tumors and their developing trajectories are convincingly realistic. Technically, we analyze and simulate tumor development at various stages using 9,262 raw, unlabeled CT images sourced from 68 hospitals worldwide. The performance in segmenting tumors in the liver, pancreas, and kidneys exceeds prevailing literature benchmarks, underlining the immense potential of tumor synthesis, especially for earlier cancer detection.   The code and models are available at https://github.com/MrGiovanni/Pixel2Cancer<|reference_end|>
@article{lai2024from,
    title={From Pixel to Cancer: Cellular Automata in Computed Tomography},
    author={Yuxiang Lai, Xiaoxi Chen, Angtian Wang, Alan Yuille, Zongwei Zhou},
    journal={arXiv preprint arXiv:2403.06459},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06459},
    primaryClass={eess.IV cs.CV}
}