AI-driven tumor analysis has garnered increasing attention in healthcare.
However, its progress is significantly hindered by the lack of annotated tumor
cases, which requires radiologists to invest a lot of effort in collecting and
annotation. In this paper, we introduce a highly practical solution for robust
tumor synthesis and segmentation, termed FreeTumor, which refers to
annotation-free synthetic tumors and our desire to free patients that suffering
from tumors. Instead of pursuing sophisticated technical synthesis modules, we
aim to design a simple yet effective tumor synthesis paradigm to unleash the
power of large-scale data. Specifically, FreeTumor advances existing methods
mainly from three aspects: (1) Existing methods only leverage small-scale
labeled data for synthesis training, which limits their ability to generalize
well on unseen data from different sources. To this end, we introduce the
adversarial training strategy to leverage large-scale and diversified unlabeled
data in synthesis training, significantly improving tumor synthesis. (2)
Existing methods largely ignored the negative impact of low-quality synthetic
tumors in segmentation training. Thus, we employ an adversarial-based
discriminator to automatically filter out the low-quality synthetic tumors,
which effectively alleviates their negative impact. (3) Existing methods only
used hundreds of cases in tumor segmentation. In FreeTumor, we investigate the
data scaling law in tumor segmentation by scaling up the dataset to 11k cases.
Extensive experiments demonstrate the superiority of FreeTumor, e.g., on three
tumor segmentation benchmarks, average $+8.9\%$ DSC over the baseline that only
using real tumors and $+6.6\%$ DSC over the state-of-the-art tumor synthesis
method. Code will be available.

AI 驱动的肿瘤分析通过引入节省肿瘤收集和注释成本的高效解决方案（FreeTumor）取得了显著进展，通过对大规模数据进行综合训练和扩展数据集，在肿瘤分割方面展现出了优越性能。

FreeTumor: 大规模肿瘤合成的先进肿瘤分割

FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis

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
this https URL

通过细胞自动机模拟肿瘤的发展过程，我们建立了一套通用规则，将肿瘤状态整合到原始计算机断层扫描图像中，生成了不同器官的合成肿瘤。经过临床读者研究和技术性评估，证明了合成肿瘤的真实性和早期癌症检测的潜力。

从像素到癌症：细胞自动机在计算机断层扫描中的应用

From Pixel to Cancer: Cellular Automata in Computed Tomography

Tumor synthesis enables the creation of artificial tumors in medical images,
facilitating the training of AI models for tumor detection and segmentation.
However, success in tumor synthesis hinges on creating visually realistic
tumors that are generalizable across multiple organs and, furthermore, the
resulting AI models being capable of detecting real tumors in images sourced
from different domains (e.g., hospitals). This paper made a progressive stride
toward generalizable tumor synthesis by leveraging a critical observation:
early-stage tumors (< 2cm) tend to have similar imaging characteristics in
computed tomography (CT), whether they originate in the liver, pancreas, or
kidneys. We have ascertained that generative AI models, e.g., Diffusion Models,
can create realistic tumors generalized to a range of organs even when trained
on a limited number of tumor examples from only one organ. Moreover, we have
shown that AI models trained on these synthetic tumors can be generalized to
detect and segment real tumors from CT volumes, encompassing a broad spectrum
of patient demographics, imaging protocols, and healthcare facilities.

利用计算机断层扫描（CT）检查中早期肿瘤的相似成像特征，本研究使用生成式人工智能模型制造现实感肿瘤，可以泛化到多个器官，并且所得到的模型可以在不同机构的 CT 图像中检测和分割真实肿瘤。