Arctique：一个统一真实性与可控性的人工组织病理学数据集，用于不确定性量化
Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification
AI总结：
Arctique是一个程序化生成的组织病理学图像数据集，通过独立控制图像与标签的不确定性，为不确定性量化方法提供兼具真实性与可控性的基准测试资源。
摘要：
不确定性量化（UQ）对于可靠的图像分割至关重要。然而，尽管该领域不断涌现新方法，但缺乏公认的基准限制了其系统比较与评估：当前的UQ方法通常要么在过于简单的玩具数据集上测试，要么在无法辨别真实不确定性的复杂真实世界数据集上测试。为了统一可控性与复杂性，我们引入了Arctique，一个模拟组织病理学结肠图像的程序化生成数据集。我们选择组织病理学图像有两个原因：1）其复杂性在于复杂的物体结构和高度可变的外观，这产生了具有挑战性的分割问题；2）其在医学诊断中的广泛普遍性以及高质量UQ的相关重要性。为了生成Arctique，我们建立了一个基于Blender的3D场景创建框架，并带有内在噪声操控。Arctique包含50,000张渲染图像，带有精确掩膜以及噪声标签模拟。我们表明，通过独立控制图像和标签中的不确定性，我们可以有效研究几种常用UQ方法的性能。因此，Arctique作为基准测试和推进复杂多目标环境中UQ技术及其他方法的关键资源，弥合了真实性与可控性之间的差距。所有代码公开可用，允许重新创建和受控操作我们提供的图像，以及创建和渲染新场景。
英文摘要：
Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are typically tested either on overly simplistic toy datasets or on complex real-world datasets that do not allow to discern true uncertainty. To unify both controllability and complexity, we introduce Arctique, a procedurally generated dataset modeled after histopathological colon images. We chose histopathological images for two reasons: 1) their complexity in terms of intricate object structures and highly variable appearance, which yields challenging segmentation problems, and 2) their broad prevalence for medical diagnosis and respective relevance of high-quality UQ. To generate Arctique, we established a Blender-based framework for 3D scene creation with intrinsic noise manipulation. Arctique contains 50,000 rendered images with precise masks as well as noisy label simulations. We show that by independently controlling the uncertainty in both images and labels, we can effectively study the performance of several commonly used UQ methods. Hence, Arctique serves as a critical resource for benchmarking and advancing UQ techniques and other methodologies in complex, multi-object environments, bridging the gap between realism and controllability. All code is publicly available, allowing re-creation and controlled manipulations of our shipped images as well as creation and rendering of new scenes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
