Multimodal image analysis in tissue diagnostics for skin melanoma

Abstract

Early diagnosis is a corner stone for a successful treatment of most diseases including melanoma, which cannot be achieved by traditional histopathological inspection. In this respect, multimodal imaging, the combination of TPEF and SHG, features a high diagnostic potential as an alternative approach.Multimodal imaging generates molecular contrast, but to use this technique in clinical practice, the optical signals must be translated into diagnostic relevant information. This translation requires automatic image analysis techniques. Within this contribution, we established an analysis pipeline for multimodal images to achieve melanoma diagnostics of skin tissue. The first step of the image analysis was the pre-treatment, where the mosaicking artifacts were corrected and a standardization was performed. Afterwards, the local histogram-based first-order texture features and the local gray-level co-occurrence matrix (GLCM) texture features were extracted in multiple scales. Thereafter, we constructed a local hierarchical statistical model to distinguish melanoma, normal epithelium, and other tissue types. The results demonstrated the capability of multimodal imaging combined with image analysis to differentiate different tissue types. Furthermore, we compared the histogram and the GLCM-based texture feature sets according to the Fisher's discriminant ratio (FDR) and the prediction of the classification, which demonstrated that the histogram-based texture features are superior to the GLCM features for the given task. Finally, we performed a global classification to achieve a patient diagnostics with the clinical diagnosis as ground truth. The agreement of the prediction and the clinical results demonstrated the great potential of multimodal imaging for melanoma diagnostics.

Skin cancer detection using non-invasive techniques

“…250 MPLSM provides high-resolution uorescence imaging, allowing visualization of cellular and subcellular structures of the epidermis and upper dermis. 251,252 MPLSM based morphologic features of skin cancer images are comparable to traditional histopathology. 252 Latest updates on multiphoton scanning for skin cancer diagnosis are summed in Table 13.…”

Invited Article: Comparison of hyperspectral coherent Raman scattering microscopies for biomedical applications

“…9 The analysis of the multispectral Raman data of biological samples requires sophisticated data analysis, no matter if spontaneous or coherent Raman scattering is used. 10,11 This is due to the fact that differences in the concentration of marker molecules like protein, DNA, and lipids are rather small for different tissue types. Therefore, appropriate spectral pre-processing steps to correct for background signals and other illumination artefacts are needed.…”

“…Therefore, appropriate spectral pre-processing steps to correct for background signals and other illumination artefacts are needed. 10,12 In this contribution, we have simultaneously recorded hyperspectral CARS and SRS datasets for head and neck tissue samples, analyzed the datasets by multispectral data analysis approaches, and compared the results.…”

Multimodal Nonlinear Microscopy for Therapy Monitoring of Cold Atmospheric Plasma Treatment

“…In this context, it has been shown that multimodal nonlinear imaging, using different methods such as coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), two-photon excited fluorescence lifetime imaging (2P-FLIM) and second harmonic generation (SHG), is a powerful tool for the label-free characterization of the molecular composition of cells and tissues, enabling the visualization of the distribution of molecular markers with subcellular spatial resolution and the correlation of their function in tissue [15,16,17,18,19]. Using machine learning image processing algorithms, the nonlinear image data can be translated into biomedical information [20,21,22,23,24].…”