Abstract
Non-destructive 3D imaging of large multi-particulate samples is essential for quantifying particle-level properties, such as size, shape, and spatial distribution, across applications in mining, materials science, and geology. However, accurate instance segmentation of particles in tomographic data remains challenging due to high morphological variability and frequent particle contact, which limit the effectiveness of classical methods like watershed algorithms. While supervised deep learning approaches offer improved performance, they rely on extensive annotated datasets that are labor-intensive, error-prone, and difficult to scale. In this work, we propose self-validated learning, a novel self-training framework for particle instance segmentation that eliminates the need for manual annotations. Our method leverages implicit boundary detection and iteratively refines the training set by identifying particles that can be consistently matched across reshuffled scans of the same sample. This self-validation mechanism mitigates the impact of noisy pseudo-labels, enabling robust learning from unlabeled data. After just three iterations, our approach accurately segments over 97% of the total particle volume and identifies more than 54,000 individual particles in tomographic scans of quartz fragments. Importantly, the framework also enables fully autonomous model evaluation without the need for ground truth annotations, as confirmed through comparisons with state-of-the-art instance segmentation techniques. The method is integrated into the Biomedisa image analysis platform (this https URL).
Abstract (translated)
非破坏性3D成像技术对于多颗粒样品的粒度属性量化（如尺寸、形状和空间分布）在矿业、材料科学和地质学等领域至关重要。然而，由于形态变化大且颗粒接触频繁，断层图像数据中的粒子实例分割仍然具有挑战性。传统的分水岭算法等方法的有效性也因此受到限制。虽然监督深度学习方法能够提高性能，但它们依赖于大量需要人工标注的数据集，这种做法耗时、易出错，并且难以扩展。 在此研究中，我们提出了一种自验证学习的新框架，用于粒子实例分割任务，这种方法可以消除手动注释的需求。我们的方法利用隐式边界检测并通过识别在重新排列的同一样本扫描中可一致匹配的颗粒来迭代地改进训练集。这种自我验证机制减少了噪声伪标签的影响，并使模型能够从无标注数据中稳健学习。 经过三次迭代后，我们提出的方法可以准确分割超过97%的总粒子体积，并且能够在石英碎片的断层图像扫描中识别出54,000多个独立颗粒。重要的是，该框架还允许完全自主地评估模型性能，无需使用真实标签注释。这已在与最新实例分割技术的对比测试中得到确认。 此方法已被集成到Biomedisa图像分析平台（点击链接查看详情）中。
URL
https://arxiv.org/abs/2508.16224
