Similarity maps and hierarchical clustering for annotating FT-IR spectral images

Zhong, Qiaoyong; Chen, Yang; Großerüschkamp, Frederik; Kallenbach-Thieltges, Angela; Serocka, Peter; Gerwert, Klaus; Mosig, Axel

doi:10.1186/1471-2105-14-333

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…Conventionally, the dendrogram of hierarchically clustered image spectra is cut "horizontally" to obtain a segmentation into a fixed number of clusters. In Zhong et al 38 however, it has been shown by one of the authors that cutting dendrograms through "non horizontal" cuts yields biologically more meaningful segmentations for IR image spectra. As our newly contributed colocalization scheme generally also identifies such non-horizontal cuts, the present study supports this claim also for Raman spectral image segmentation.…”

Section: Discussionmentioning

confidence: 99%

Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study

Krauß

Petersen

Niedieker

et al. 2015

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A major promise of Raman microscopy is the label-free detailed recognition of cellular and subcellular structures. To this end, identifying colocalization patterns between Raman spectral images and fluorescence microscopic images is a key step to annotate subcellular components in Raman spectroscopic images. While existing approaches to resolve subcellular structures are based on fluorescence labeling, we propose a combination of a colocalization scheme with subsequent training of a supervised classifier that allows label-free resolution of cellular compartments. Our colocalization scheme unveils statistically significant overlapping regions by identifying correlation between the fluorescence color channels and clusters from unsupervised machine learning methods like hierarchical cluster analysis. The colocalization scheme is used as a pre-selection to gather appropriate spectra as training data. These spectra are used in the second part as training data to establish a supervised random forest classifier to automatically identify lipid droplets and nucleus. We validate our approach by examining Raman spectral images overlaid with fluorescence labelings of different cellular compartments, indicating that specific components may indeed be identified label-free in the spectral image. A Matlab implementation of our colocalization software is available at .

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Section: Discussionmentioning

confidence: 99%

Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study

Krauß

Petersen

Niedieker

et al. 2015

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“…Subsequently, multivariate methods were applied to the analysis of imaging data sets. [17][18][19] In one of these methods (known as unsupervised hierarchical cluster analysis [HCA]) 20 the entire spectrum is used, and a correlation of each spectrum with all other spectra in the data set is performed. Subsequently, the values of the correlation coefficients may be displayed at the pixel coordinates, again as grayscale or pseudocolor images, to reveal similarity of the pixel spectra.…”

Section: Spectral Histopathologymentioning

confidence: 99%

Resolving Interobserver Discrepancies in Lung Cancer Diagnoses by Spectral Histopathology

Akalin

Ergin

Remiszewski

et al. 2018

Archives of Pathology &Amp; Laboratory Medicine

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This paper reports the results of a collaborative lung cancer study between City of Hope Cancer Center (Duarte, California) and CIRECA, LLC (Cambridge, Massachusetts), comprising 328 samples from 249 patients, that used an optical technique known as spectral histopathology (SHP) for tissue classification. Because SHP is based on a physical measurement, it renders diagnoses on a more objective and reproducible basis than methods based on assessing cell morphology and tissue architecture. This report demonstrates that SHP provides distinction of adenocarcinomas from squamous cell carcinomas of the lung with an accuracy comparable to that of immunohistochemistry and highly reliable classification of adenosquamous carcinoma. Furthermore, this report shows that SHP can be used to resolve interobserver differences in lung pathology. Spectral histopathology is based on the detection of changes in biochemical composition, rather than morphologic features, and is therefore more akin to methods such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry imaging. Both matrix-assisted laser desorption ionization time-of-flight mass spectrometry and SHP imaging modalities demonstrate that changes in tissue morphologic features observed in classical pathology are accompanied by, and may be correlated to, changes in the biochemical composition at the cellular level. Thus, these imaging methods provide novel insight into biochemical changes due to disease.

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“…On the other hand, it is well-established that clustering unveils tissue structure in FTIR, Raman and CARS images relatively well in general, and is in fact commonly used for spectral image segmentation. While supervised or interactive [ 21 , 22 ] approaches are known to achieve segmentations that display biologically relevant structures more accurately, such approaches require either prior knowledge or manual interactions, contradicting our goal of achieving a fully unsupervised registration. It is also known that hierarchical clustering represents tissue structure better than non-hierarchical approaches [ 21 ], but at the cost of significantly higher demands in running time and memory.…”

Section: Introductionmentioning

confidence: 99%

Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections

et al. 2015

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BackgroundIn recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools.ResultsWe propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images.ConclusionsOur validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0804-9) contains supplementary material, which is available to authorized users.

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Similarity maps and hierarchical clustering for annotating FT-IR spectral images

Cited by 9 publications

References 24 publications

Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study

Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study

Resolving Interobserver Discrepancies in Lung Cancer Diagnoses by Spectral Histopathology

Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections

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