Center for Industrial Mathematics, University of Bremen, Bremen, Germany.
J Proteomics. 2011 Dec 10;75(1):237-45. doi: 10.1016/j.jprot.2011.08.002. Epub 2011 Aug 11.
In the last decade, imaging mass spectrometry has seen incredible technological advances in its applications to biological samples. One computational method of data mining in this field is the spatial segmentation of a sample, which produces a segmentation map highlighting chemically similar regions. An important issue for any imaging mass spectrometry technology is its relatively low spatial or lateral resolution (i.e. a large size of pixel) as compared with microscopy. Thus, the spatial resolution of a segmentation map is also relatively low, that complicates its visual examination and interpretation when compared with microscopy data, as well as reduces the accuracy of any automated comparison. We address this issue by proposing an approach to improve the spatial resolution of a segmentation map. Given a segmentation map, our method magnifies it up to some factor, producing a super-resolution segmentation map. The super-resolution map can be overlaid and compared with a high-res microscopy image. The proposed method is based on recent advances in image processing and smoothes the "pixilated" region boundaries while preserving fine details. Moreover, it neither eliminates nor splits any region. We evaluated the proposed super-resolution segmentation approach on three MALDI-imaging datasets of human tissue sections and demonstrated the superiority of the super-segmentation maps over standard segmentation maps.
在过去的十年中，成像质谱技术在生物样本的应用方面取得了令人瞩目的技术进步。该领域中数据挖掘的一种计算方法是对样本进行空间分割，生成突出化学相似区域的分割图。对于任何成像质谱技术来说，一个重要问题是其空间或横向分辨率相对较低（即像素尺寸较大），与显微镜相比。因此，分割图的空间分辨率也相对较低，这使得与显微镜数据相比，其视觉检查和解释变得更加复杂，并且降低了任何自动比较的准确性。我们通过提出一种提高分割图空间分辨率的方法来解决这个问题。给定一个分割图，我们的方法将其放大到某个因子，生成一个超分辨率分割图。超分辨率地图可以覆盖并与高分辨率显微镜图像进行比较。所提出的方法基于图像处理的最新进展，在保留细微细节的同时平滑“像素化”区域边界。此外，它既不会消除也不会分割任何区域。我们在三个人类组织切片的 MALDI 成像数据集上评估了所提出的超分辨率分割方法，并证明了超分割图优于标准分割图。
