Abstract
Organoids, sophisticated in vitro models of human tissues, are crucial for medical research due to their ability to simulate organ functions and assess drug responses accurately. Accurate organoid instance segmentation is critical for quantifying their dynamic behaviors, yet remains profoundly limited by high-quality annotated datasets and pervasive overlap in microscopy imaging. While semi-supervised learning (SSL) offers a solution to alleviate reliance on scarce labeled data, conventional SSL frameworks suffer from biases induced by noisy pseudo-labels, particularly in overlapping regions. Synthesis-assisted SSL (SA-SSL) has been proposed for mitigating training biases in semi-supervised semantic segmentation. We present the first adaptation of SA-SSL to organoid instance segmentation and reveal that SA-SSL struggles to disentangle intertwined organoids, often misrepresenting overlapping instances as a single entity. To overcome this, we propose Pseudo-Label Unmixing (PLU), which identifies erroneous pseudo-labels for overlapping instances and then regenerates organoid labels through instance decomposition. For image synthesis, we apply a contour-based approach to synthesize organoid instances efficiently, particularly for overlapping cases. Instance-level augmentations (IA) on pseudo-labels before image synthesis further enhances the effect of synthetic data (SD). Rigorous experiments on two organoid datasets demonstrate our method's effectiveness, achieving performance comparable to fully supervised models using only 10% labeled data, and state-of-the-art results. Ablation studies validate the contributions of PLU, contour-based synthesis, and augmentation-aware training. By addressing overlap at both pseudo-label and synthesis levels, our work advances scalable, label-efficient organoid analysis, unlocking new potential for high-throughput applications in precision medicine.
Abstract (translated)
类器官，即人类组织的复杂体外模型，在医学研究中至关重要，因为它们能够模拟器官功能并准确评估药物反应。精确的类器官实例分割对于量化其动态行为是至关重要的，但这一过程受到高质量标注数据集稀缺以及显微图像重叠现象的影响而受限。虽然半监督学习（SSL）可以作为缓解对稀有标签数据依赖的一种解决方案，但是传统的SSL框架因噪声伪标签所诱导的偏差问题，在重叠区域尤为明显。合成辅助的半监督学习（SA-SSL）被提出用于减轻半监督语义分割中的训练偏差。我们首次将SA-SSL应用于类器官实例分割，并揭示出SA-SSL在区分缠绕在一起的类器官时存在困难，往往将重叠实例误判为单一实体。 为了克服这个问题，我们提出了伪标签解混（PLU），这种方法能够识别重叠实例上的错误伪标签，并通过实例分解来再生类器官标签。对于图像合成，我们采用了一种基于轮廓的方法，以高效地生成类器官实例，特别是针对重叠情况特别有效。在进行图像合成之前，在伪标签上应用实例级别增强（IA）进一步增强了合成数据的效果。 我们在两个类器官数据集上的严格实验表明了该方法的有效性，使用仅10%的标注数据便能达到与全监督模型相媲美的性能，并且取得了最新的研究成果。消融研究表明PLU、基于轮廓的合成以及感知增益训练的贡献是有效的。通过在伪标签和合成水平上解决重叠问题，我们的工作推进了大规模高效标记类器官分析的发展，为精准医疗中的高通量应用解锁了新的潜力。
URL
https://arxiv.org/abs/2601.06642
