Synth-Colon

Download dataset (41.2 GB): https://enric1994.github.io/synth-colon/

Synth-Colon is a synthetic dataset for polyp segmentation. It is the first dataset generated using zero annotations from medical
professionals. The dataset is composed of 20 000 images with a resolution of 500×500. Synth-Colon additionally includes realistic colon images
generated with our CycleGAN and the Kvasir training set images. Additionally, Synth-Colon can also be used for the colon depth estimation task because it includes depth and 3D information for each image. Synth-Colon includes:

• Synthetic images of the colon and one polyp.
• Masks indicating the location of the polyp.
• Realistic images of the colon and polyps generated using our CycleGAN baseline and the Kvasir dataset.
• Depth images of the colon and polyp.
• 3D meshes of the colon and polyp in OBJ format.

The 3D colon and polyps are procedurally generated using Blender, a 3D engine that can be automated via scripting. Our 3D colon structure is
a cone composed by 2454 faces. Vertices are randomly displaced following a uniform distribution in order to simulate the tissues in the colon.
Additionally, the colon structure is modified by displacing 7 segments. For the textures we used a base color [0.80, 0.13, 0.18] (RGB).
For each sample we shift the color to other tones by adding a 20% of uniform noise to each channel. One single polyp is used on every image,
which is placed inside the colon. It can be either in the colon’s walls or in the middle. Polyps are distorted spheres with 16384 faces. Samples with
polyps occupying less than 2.6% of the image are removed. This results in a dataset average polyp size of 5.87%, which is within the values of the
real datasets: the dataset with the smallest average polyp size is CVC-300 with 3.36% and the largest is Kvasir with 16.46%.
Lighting is composed by a white ambient light, two white dynamic lights that project glare into the walls, and three negative lights that project
black light at the end of the colon. We found that having a dark area at the end helps the generative models to understand the structure of the colon. The 3D scene must be similar to real colon images or the models will not properly translate the images to the real-world domain.

Dataset website: https://enric1994.github.io/synth-colon/
Source code: https://github.com/enric1994/synth-colon

下载数据集（41.2 GB）：https://enric1994.github.io/synth-colon/
Synth-Colon 是一种用于息肉分割的合成数据集。它是首个由医疗专业人员零标注生成的数据集。该数据集由 20,000 张分辨率为 500×500 的图像组成。Synth-Colon 此外还包括了我们利用 CycleGAN 和 Kvasir 训练集生成的逼真结肠图像。此外，Synth-Colon 还可用于结肠深度估计任务，因为它包含了每张图像的深度和三维信息。Synth-Colon 包含以下内容：

• 结肠和单个息肉的合成图像。
• 指示息肉位置的掩码。
• 使用我们的 CycleGAN 基线和 Kvasir 数据集生成的逼真结肠和息肉图像。
• 结肠和息肉的深度图像。
• 以 OBJ 格式提供的结肠和息肉的三维网格。

3D 结肠和息肉是通过 Blender 生成的，Blender 是一种可以通过脚本自动化的三维引擎。我们的 3D 结肠结构由 2454 个面组成的圆锥体构成。顶点按照均匀分布随机偏移，以模拟结肠中的组织。此外，通过偏移 7 个部分对结肠结构进行修改。对于纹理，我们使用了基础颜色 [0.80, 0.13, 0.18]（RGB）。对于每个样本，我们通过向每个通道添加 20% 的均匀噪声来调整颜色至其他色调。每张图像上使用一个单独的息肉，放置在结肠内部，它可能位于结肠壁上或中间。息肉是扭曲的球体，具有 16384 个面。去除图像中息肉占比较少的样本，即少于 2.6% 的样本。这导致数据集中息肉的平均大小为 5.87%，该值位于真实数据集的范围内：平均息肉尺寸最小的数据集是 CVC-300，占 3.36%，而最大的则是 Kvasir，占 16.46%。

照明由白色环境光、两个投射到墙壁上的白色动态光和三个在结肠末端投射黑色光的负光组成。我们发现，在末端拥有一个暗区有助于生成模型理解结肠的结构。三维场景必须与真实结肠图像相似，否则模型无法正确地将图像转换为现实世界领域。

数据集网站：https://enric1994.github.io/synth-colon/
源代码：https://github.com/enric1994/synth-colon

搜集汇总

背景与挑战

背景概述

Synth-Colon是一个包含2万张图像的合成息肉分割数据集，首创无需医学标注的生成方式，提供图像、掩码、深度图和3D网格等多模态数据，适用于息肉分割和结肠深度估计研究。