Dávid Csaba, Giber Kristóf, Kerti-Szigeti Katalin, Köllő Mihály, Nusser Zoltan, Acsady Laszlo
Lendület Laboratory of Thalamus Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary.
Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary.
Elife. 2024 Dec 10;12:RP89361. doi: 10.7554/eLife.89361.
Unsupervised segmentation in biological and non-biological images is only partially resolved. Segmentation either requires arbitrary thresholds or large teaching datasets. Here, we propose a spatial autocorrelation method based on Local Moran's coefficient to differentiate signal, background, and noise in any type of image. The method, originally described for geoinformatics, does not require a predefined intensity threshold or teaching algorithm for image segmentation and allows quantitative comparison of samples obtained in different conditions. It utilizes relative intensity as well as spatial information of neighboring elements to select spatially contiguous groups of pixels. We demonstrate that Moran's method outperforms threshold-based method in both artificially generated as well as in natural images especially when background noise is substantial. This superior performance can be attributed to the exclusion of false positive pixels resulting from isolated, high intensity pixels in high noise conditions. To test the method's power in real situation, we used high power confocal images of the somatosensory thalamus immunostained for Kv4.2 and Kv4.3 (A-type) voltage-gated potassium channels in mice. Moran's method identified high-intensity Kv4.2 and Kv4.3 ion channel clusters in the thalamic neuropil. Spatial distribution of these clusters displayed strong correlation with large sensory axon terminals of subcortical origin. The unique association of the special presynaptic terminals and a postsynaptic voltage-gated ion channel cluster was confirmed with electron microscopy. These data demonstrate that Moran's method is a rapid, simple image segmentation method optimal for variable and high noise conditions.
生物和非生物图像中的无监督分割仅得到部分解决。分割要么需要任意阈值，要么需要大量的训练数据集。在这里，我们提出一种基于局部莫兰系数的空间自相关方法，以区分任何类型图像中的信号、背景和噪声。该方法最初是为地理信息学描述的，不需要预定义的强度阈值或用于图像分割的训练算法，并且允许对在不同条件下获得的样本进行定量比较。它利用相邻元素的相对强度以及空间信息来选择空间上连续的像素组。我们证明，莫兰方法在人工生成的图像和自然图像中均优于基于阈值的方法，尤其是在背景噪声很大时。这种优越的性能可归因于在高噪声条件下排除了由孤立的高强度像素导致的假阳性像素。为了测试该方法在实际情况中的效能，我们使用了对小鼠体感丘脑进行Kv4.2和Kv4.3（A型）电压门控钾通道免疫染色的高分辨率共聚焦图像。莫兰方法识别出丘脑神经纤维网中高强度的Kv4.2和Kv4.3离子通道簇。这些簇的空间分布与皮质下起源的大型感觉轴突终末显示出强烈的相关性。通过电子显微镜证实了特殊突触前终末与突触后电压门控离子通道簇的独特关联。这些数据表明，莫兰方法是一种快速、简单的图像分割方法，最适用于可变和高噪声条件。
