Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool.

BACKGROUND: Due to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo-pathological stained tissue sections. To save valuable time, low quality cryosections are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue sections. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making. METHODS: In this contribution we will show that the combination of the three label-free non-linear imaging modalities CARS (coherent anti-Stokes Raman-scattering), TPEF (two-photon excited autofluorescence) and SHG (second harmonic generation) yields information that can be translated into computational hematoxylin and eosin (HE) images by multivariate statistics. Thereby, a computational HE stain is generated resulting in pseudo-HE overview images that allow for identification of suspicious regions. The latter are analyzed further by Raman-spectroscopy retrieving the tissue's molecular fingerprint. RESULTS: The results suggest that the combination of non-linear multimodal imaging and Raman-spectroscopy possesses the potential as a precise and fast tool in routine histopathology. CONCLUSIONS: As the key advantage, both optical methods are non-invasive enabling for further pathological investigations of the same tissue section, e.g. a direct comparison with the current pathological gold-standard.

Systematic evaluation of the biological variance within the Raman based colorectal tissue diagnostics.

Being among the most common cancers worldwide screening and early diagnosis of colorectal cancer is of high interest for the health system, the patients and for research. Raman microspectroscopy as a label-free, non-invasive and non-destructive technique is a promising tool for an early diagnosis. However, to ensure a reliable diagnosis specially designed statistical analysis workflows are required. Several statistical approaches have been introduced leading to varying results in the overall accuracy, sensitivity and specificity. In this study a systematic evaluation of different statistical analysis approaches has been performed using a colon cancer mouse model with genotypic identical individuals. Based on the inter-individual Raman spectral variances a measure for the biological variance can be estimated. By applying a leave-one-individual-out cross-validation a clinically relevant discrimination of healthy tissue versus adenoma and carcinoma with an accuracy of 95% is shown. Furthermore, the transfer of a model from tissue to biopsy specimen is demonstrated.

Multimodal Imaging Spectroscopy of Tissue.

Advanced optical imaging technologies have experienced increased visibility in medical research, as they allow for a label-free and nondestructive investigation of tissue in either an excised state or living organisms. In addition to a multitude of ex vivo studies proving the applicability of these optical imaging approaches, a transfer of various modalities toward in vivo diagnosis is currently in progress as well. Furthermore, combining optical imaging techniques, referred to as multimodal imaging, allows for an improved diagnostic reliability due to the complementary nature of retrieved information. In this review, we provide a summary of ongoing multifold efforts in multimodal tissue imaging and focus in particular on in vivo applications for medical diagnosis. We also discuss the advantages and potential limitations of the imaging methods and outline opportunities for future developments.

Automated seeding-based nuclei segmentation in nonlinear optical microscopy.

Nonlinear optical (NLO) microscopy based, e.g., on coherent anti-Stokes Raman scattering (CARS) or two-photon-excited fluorescence (TPEF) is a fast label-free imaging technique, with a great potential for biomedical applications. However, NLO microscopy as a diagnostic tool is still in its infancy; there is a lack of robust and durable nuclei segmentation methods capable of accurate image processing in cases of variable image contrast, nuclear density, and type of investigated tissue. Nonetheless, such algorithms specifically adapted to NLO microscopy present one prerequisite for the technology to be routinely used, e.g., in pathology or intraoperatively for surgical guidance. In this paper, we compare the applicability of different seeding and boundary detection methods to NLO microscopic images in order to develop an optimal seeding-based approach capable of accurate segmentation of both TPEF and CARS images. Among different methods, the Laplacian of Gaussian filter showed the best accuracy for the seeding of the image, while a modified seeded watershed segmentation was the most accurate in the task of boundary detection. The resulting combination of these methods followed by the verification of the detected nuclei performs high average sensitivity and specificity when applied to various types of NLO microscopy images.

From molecular structure to tissue architecture: collagen organization probed by SHG microscopy.

Second-harmonic generation (SHG) microscopy is a fantastic tool for imaging collagen and probing its hierarchical organization from molecular scale up to tissue architectural level. In fact, SHG combines the advantages of a non-linear microscopy approach with a coherent modality able to probe molecular organization. In this manuscript we review the physical concepts describing SHG from collagen, highlighting how this optical process allows to probe structures ranging from molecular sizes to tissue architecture, through image pattern analysis and scoring methods. Starting from the description of the most relevant approaches employing SHG polarization anisotropy and forward - backward SHG detection, we then focus on the most relevant methods for imaging and characterizing collagen organization in tissues through image pattern analysis methods, highlighting advantages and limitations of the methods applied to tissue imaging and to potential clinical applications.

Detection and Discrimination of Non-Melanoma Skin Cancer by Multimodal Imaging.

Non-melanoma skin cancer (NMSC) belongs to the most frequent human neoplasms. Its exposed location facilitates a fast ambulant treatment. However, in the clinical practice far more lesions are removed than necessary, due to the lack of an efficient pre-operational examination procedure: Standard imaging methods often do not provide a sufficient spatial resolution. The demand for an efficient in vivo imaging technique might be met in the near future by non-linear microscopy. As a first step towards this goal, the appearance of NMSC in various microspectroscopic modalities has to be defined and approaches have to be derived to distinguish healthy skin from NMSC using non-linear optical microscopy. Therefore, in this contribution the appearance of ex vivo NMSC in a combination of coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG) and two photon excited fluorescence (TPEF) imaging-referred as multimodal imaging-is described. Analogous to H&E staining, an overview of the distinct appearances and features of basal cell and squamous cell carcinoma in the complementary modalities is derived, and is expected to boost in vivo studies of this promising technological approach.

The invention of immersion ultramicroscopy in 1912-the birth of nanotechnology?

Dawn of nanotechnology: the immersion ultramicroscope was patented a century ago. When an analyte was examined with an antique instrument and with state-of-the-art technology, the historic assumptions were confirmed: the size and shape of the nanoparticles are in the same range as that described 100 years ago. The spectra of the Tyndall cones caused by the shape of the nanoparticles were also described correctly-long before electron microscopy was able to image single nanoparticles.

Automated classification of healthy and keloidal collagen patterns based on processing of SHG images of human skin.

All-optical microspectroscopic and tomographic tools have a great potential for the clinical investigation of human skin and skin diseases. However, automated optical tomography or even microscopy generate immense data sets. Therefore, in order to implement such diagnostic tools into the medical practice in both hospitals and private practice, there is a need for automated data handling and image analysis ideally implementing automized scores to judge the physiological state of a tissue section. In this contribution, the potential of an image processing algorithm for the automated classification of skin into normal or keloid based on second-harmonic generation (SHG) microscopic images is demonstrated. Such SHG data is routinely recorded within a multimodal imaging approach. The classification of the tissue implemented in the algorithm employs the geometrical features of collagen patterns that differ depending on the constitution, i.e., physiological status of the skin.

Toward in vivo chemical imaging of epicuticular waxes.

Epicuticular waxes, which are found on the outer surface of plant cuticles, are difficult to study in vivo. To monitor the growth, development, and structural alterations of epicuticular wax layers, coherent anti-Stokes Raman scattering (CARS) might be used. CARS, as a Raman-based technique, not only provides structural insight but also chemical information by imaging the spatial distribution of Raman-active vibrations. Here, we present a comparative study using CARS and scanning electron microscopy to characterize the structure of epicuticular waxes. The ability of CARS to provide detailed structural information on the biologically important wax layer was detailed on the examples of cherry laurel (Prunus laurocerasus), hoya (Hoya carnosa), and ceriman/Swiss cheese plant (Monstera sp. aff. deliciosa). We anticipate that the work presented will open a doorway for online monitoring of formation and alterations of epicuticular wax layers.

Multimodal imaging to study the morphochemistry of basal cell carcinoma.

Basal cell carcinoma is the most abundant malignant neoplasm in humans, the pathology of which is characterized by an abnormal proliferation of basal cells. Basal cell carcinoma can show a variety of different morphologies, which are based on different cellular biology. Furthermore, the carcinoma often grows invisibly to the eye imbedded in the surrounding skin. Therefore, in some cases its clinical detection is challenging. Thus, our work aims at establishing an unsupervised tissue classification method based on multimodal imaging and the application of chemometrics to discriminate basal cell carcinoma from non-diseased tissue. A case study applying multimodal imaging to ex-vivo sections of basal cell carcinoma is presented. In doing so, we apply a combination of various linear and non-linear imaging modalities, i.e. fluorescence, Raman and second-harmonic generation microscopy, to study the morphochemistry of basal cell carcinoma. The joint information content obtained by such multimodal approach in studying various aspects of the malignant tissue alterations associated with basal cell carcinoma is discussed.

Separation of CARS image contributions with a Gaussian mixture model.

Coherent anti-Stokes Raman scattering (CARS) gained a lot of importance in chemical imaging. This is due to the fast image acquisition time, the high spatial resolution, the non-invasiveness, and the molecular sensitivity of this method. By using the single-line CARS in contrast to the multiplex CARS, different signal contributions stemming from resonant and non-resonant light-matter interactions are indistinguishable. Here a numerical method is presented in order to extract more information from univariate CARS images: vibrational composition, morphological information, and contributions from index-of-refraction steps can be separated from single-line CARS images. The image processing algorithm is based on the physical properties of CARS process as reflected in the shape of the intensity histogram of univariate CARS images. Because of this the comparability of individual CARS images recorded with different experimental parameters is achieved. The latter is important for a quantitative evaluation of CARS images.

Analysis of the cytochrome distribution via linear and nonlinear Raman spectroscopy.

The cytochrome distribution in hyphal tip cells of Schizophyllum commune was visualized using resonance Raman mapping and CARS microscopy. For comparison, resonance Raman mapping and CARS imaging of cytochrome was also performed during branch formation and in completely developed central hyphae. Cytochrome, as an essential component of the electron transport chain in mitochondria, plays an important role in providing energy to actively growing mycelia. It could be shown that mitochondria at the growing hyphal tips and at branching regions are more active, i.e. contain more cytochrome, as compared to those in older parts of the hyphae. This finding is compatible with the idea of high energy consumption for biosynthesis and intracellular transport at the growing tip, while older hyphae have lower needs for ATP generated via the respiratory chain in mitochondria. To the best of our knowledge this is the first study reporting about the localization and distribution of cytochrome, as an indirect mitochondria localization within S. commune or other basidiomycetous fungi, by means of resonance Raman microspectroscopy and CARS microscopy. These Raman methods bear the potential of label-free in vivo mitochondria localization and investigation.

A comparative Raman and CARS imaging study of colon tissue.

An experimental evaluation of the information content of two complimentary techniques, linear Raman and coherent anti-Stokes Raman scattering (CARS) microscopy, is presented. CARS is a nonlinear variant of Raman spectroscopy that enables rapid acquisition of images within seconds in combination with laser scanning microscopes. CARS images were recorded from thin colon tissue sections at 2850, 1660, 1450 and 1000 cm(-1) and compared with Raman images. Raman images were obtained from univariate and multivariate (k-means clustering) methods, whereas all CARS images represent univariate results. Variances within tissue sections could be visualized in chemical maps of CARS and Raman images. However, identification of tissue types and characterization of variances between different tissue sections were only possible by analysis of cluster mean spectra, obtained from k-means cluster analysis. This first comparison establishes the foundation for further development of the CARS technology to assess tissue.