Combined information from Raman spectroscopy and optical coherence tomography for enhanced diagnostic accuracy in tissue discrimination

We thank the UK EPSRC for funding, the CR-UK/EPSRC/MRC/DoH (England) imaging programme, the European Union project FAMOS (FP7 ICT, contract no. 317744) and the European Union project IIIOS (FP7/2007-2013, contract no. 238802). We thank Tayside Tissue Bank for providing us with the tissue samples under request number TR000289. K.D. is a Royal Society-Wolfson Merit Award Holder.Optical spectroscopy and imaging methods have proved to have potential to discriminate between normal and abnormal tissue types through minimally invasive procedures. Raman spectroscopy and Optical Coherence Tomography (OCT) provides chemical and morphological information of tissues respectively, which are complementary to each other. When used individually they might not be able to obtain high enough sensitivity and specificity that is clinically relevant. In this study we combined Raman spectroscopy information with information obtained from OCT to enhance the sensitivity and specificity in discriminating between Colonic Adenocarcinoma from Normal Colon. OCT being an imaging technique, the information from this technique is conventionally analyzed qualitatively. To combine with Raman spectroscopy information, it was essential to quantify the morphological information obtained from OCT. Texture analysis was used to extract information from OCT images, which in-turn was combined with the information obtained from Raman spectroscopy. The sensitivity and specificity of the classifier was estimated using leave one out cross validation (LOOCV) method where support vector machine (SVM) was used for binary classification of the tissues. The sensitivity obtained using Raman spectroscopy and OCT individually was 89% and 78% respectively and the specificity was 77% and 74% respectively. Combining the information derived using the two techniques increased both sensitivity and specificity to 94% demonstrating that combining complementary optical information enhances diagnostic accuracy. These results demonstrate that a multimodal approach using Raman-OCT would be able to enhance the diagnostic accuracy for identifying normal and cancerous tissue types.Publisher PD

Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Morphological Features of Dysplastic Progression in Epithelium: Quantification of Cytological, Microendoscopic, and Second Harmonic Generation Images

Advances in imaging technology have led to a variety of available clinical and investigational systems. In this collection of studies, we tested the relevance of morphological image feature quantification on several imaging systems and epithelial tissues. Quantification carries the benefit of creating numerical baselines and thresholds of healthy and abnormal tissues, to potentially aid clinicians in determining a diagnosis, as well as providing researchers with standardized, unbiased results for future dissemination and comparison. Morphological image features in proflavine stained oral cells were compared qualitatively to traditional Giemsa stained cells, and then we quantified the nuclear to cytoplasm ratio. We determined that quantification of proflavine stained cells matched our hypothesis, as the nuclei in oral carcinoma cells were significantly larger than healthy oral cells. Proflavine has been used in conjunction with translational fluorescence microendoscopy of the gastrointestinal tract, and we demonstrated the ability of our custom algorithm to accurately (up to 85% sensitivity) extract colorectal crypt area and circularity data, which could minimize the burden of training on clinicians. In addition, we proposed fluorescein as an alternative fluorescent dye, providing comparable crypt area and circularity information. In order to investigate the morphological changes of crypts via the supporting collagen structures, we adapted our quantification algorithm to analyze crypt area, circularity, and an additional shape parameter in second harmonic generation images of label-free freshly resected murine epithelium. Murine models of colorectal cancer (CRC) were imaged at early and late stages of tumor progression, and we noted significant differences between the Control groups and the late cancer stages, with some differences between early and late stages of CRC progression

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe