发表机构
The Czech Academy of Sciences, Institute of Information Theory and Automation; Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering; University of Chemistry and Technology Prague, Department of Biochemistry and Microbiology; Charles University, Faculty of Science, BIOCEV（捷克科学院，信息理论与自动化研究所; 布拉格捷克技术大学，核科学与物理工程学院; 布拉格化学与技术大学，生物化学与微生物学系; 查理大学，科学学院，BIOCEV）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究少样本球体分割问题，提出可微经典图像处理算子库HyperBank，通过在支持图像上拟合并在验证图像上评估，发现其在小簇、对比度驱动数据上有竞争力，且能揭示少样本信号分布及受支持集形态学影响。
AI 中文摘要
少样本球体分割必须仅从一小部分带注释的图像适应新的细胞系、显微镜和照明条件。虽然基础少样本分割器可能很准确，但其庞大不透明的主干使得难以理解哪些视觉线索导致成功或失败。我们使用HyperBank研究这个问题，它是一个结合了Frangi血管性、Sauvola阈值金字塔、结构张量响应、梯度幅度和高斯拉普拉斯滤波器的可微经典图像处理算子库。HyperBank在带注释的支持图像上进行拟合，并在三个独立获取的球体数据集的不相交验证图像上进行评估。我们不将其视为基础模型的一般替代品，而是视为一个紧凑、可解释的少样本显微镜管道以及对哪些经典线索携带少样本信号的分析先验探测器。结果表明，在相同的少量带注释支持图像上进行适应时，一个紧凑的分析先验库与大得多的基础模型具有竞争力，并且在小簇、对比度驱动的数据上可以优于它们，而那些模型在外部来源、纹理主导的球体上仍然更强。留一类别消融表明有用的少样本信号分布在算子类别中，并通过支持集调整的形态学得到加强。
英文摘要
Few-shot spheroid segmentation must adapt to new cell lines, microscopes, and illumination conditions from only a small set of annotated images. While foundation few-shot segmenters can be accurate, their large opaque backbones make it difficult to understand which visual cues drive success or failure. We study this question with HyperBank, a differentiable bank of classical image-processing operators combining Frangi vesselness, a Sauvola threshold pyramid, structure-tensor responses, gradient magnitude, and Laplacian-of-Gaussian filters. HyperBank is fitted on the annotated support images and evaluated on disjoint held-out images across three independently acquired spheroid datasets. We treat it not as a general replacement for foundation models, but as a compact, interpretable few-shot microscopy pipeline and an analytic-prior probe of which classical cues carry the few-shot signal. The results show that, adapted on the same few annotated support images, a compact bank of analytic priors is competitive with, and on small-cluster, contrast-driven data can outperform, much larger foundation models, while those models remain stronger on externally sourced, texture-dominated spheroids. Leave-one-family-out ablations indicate that the useful few-shot signal is distributed across operator families and strengthened by support-set-tuned morphology.
CommentsAccepted for publication in the IEEE Xplore ICIP 2026 Workshop Proceedings, Computational Optical Microscopy Satellite Workshop of the 2026 IEEE International Conference on Image Processing (ICIP), Tampere, Finland
