From Pixel to Cancer: Cellular Automata in Computed Tomography
Yuxiang Lai Xiaoxi Chen Angtian Wang Alan Yuille Zongwei Zhou
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 generate tumors at varied stages in 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