Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis.

Colorectal cancer presents one of the most elevated incidences of cancer worldwide. Colonoscopy relies on histopathology analysis of hematoxylin-eosin (H&E) images of the removed tissue. Novel techniques such as multi-photon microscopy (MPM) show promising results for performing real-time optical biopsies. However, clinicians are not used to this imaging modality and correlation between MPM and H&E information is not clear. The objective of this paper is to describe and make publicly available an extensive dataset of fully co-registered H&E and MPM images that allows the research community to analyze the relationship between MPM and H&E histopathological images and the effect of the semantic gap that prevents clinicians from correctly diagnosing MPM images. The dataset provides a fully scanned tissue images at 10x optical resolution (0.5 µm/px) from 50 samples of lesions obtained by colonoscopies and colectomies. Diagnostics capabilities of TPF and H&E images were compared. Additionally, TPF tiles were virtually stained into H&E images by means of a deep-learning model. A panel of 5 expert pathologists evaluated the different modalities into three classes (healthy, adenoma/hyperplastic, and adenocarcinoma). Results showed that the performance of the pathologists over MPM images was 65% of the H&E performance while the virtual staining method achieved 90%. MPM imaging can provide appropriate information for diagnosing colorectal cancer without the need for H&E staining. However, the existing semantic gap among modalities needs to be corrected.

Analysis on the Characterization of Multiphoton Microscopy Images for Malignant Neoplastic Colon Lesion Detection under Deep Learning Methods.

BACKGROUND: Colorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately, its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory. AIMS: In this paper, we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue, without the need for histopathological staining. MATERIALS AND METHODS: To this end, we present a novel MPM public dataset containing 14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially coherent predictions scheme is applied for performance improvement. RESULTS: We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self-confidence of the network on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified as uncertain. CONCLUSIONS: This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.

Correlative Brillouin and Raman spectroscopy data acquired on single cells.

The distribution of chemical species and the mechanical modulation inside a single cell or tissue are of fundamental importance to characterize their physiological activity or their pathological conditions [1-4]. Here we analyse these properties by means of label free, non invasive, spectroscopic methods. In particular, we use a recently developed micro-spectrometer, which acquires simultaneously Raman and Brillouin spectra on the same point with subcellular resolution [5]. The techniques ability to analyse the chemical composition and the mechanical properties of single cells has been tested on NIH/3T3 murine fibroblast cells grown in adhesion on silicon substrates. Here we report the data acquired from fixed cells after their oncogenic transformation. Mechanical and chemical evolution is evident by direct inspection of raw data. Sharing our experimental records can be valuable for researchers interested in the analysis of single cells by Raman and Brillouin spectroscopy in order: i) to compare data acquired by different set-ups and ii) to correctly model the fitting functions.

Non-contact mechanical and chemical analysis of single living cells by microspectroscopic techniques.

Innovative label-free microspectroscopy, which can simultaneously collect Brillouin and Raman signals, is used to characterize the viscoelastic properties and chemical composition of living cells with sub-micrometric resolution. The unprecedented statistical accuracy of the data combined with the high-frequency resolution and the high contrast of the recently built experimental setup permits the study of single living cells immersed in their buffer solution by contactless measurements. The Brillouin signal is deconvoluted in the buffer and the cell components, thereby revealing the mechanical heterogeneity inside the cell. In particular, a 20% increase is observed in the elastic modulus passing from the plasmatic membrane to the nucleus as distinguished by comparison with the Raman spectroscopic marker. Brillouin line shape analysis is even more relevant for the comparison of cells under physiological and pathological conditions. Following oncogene expression, cells show an overall reduction in the elastic modulus (15%) and apparent viscosity (50%). In a proof-of-principle experiment, the ability of this spectroscopic technique to characterize subcellular compartments and distinguish cell status was successfully tested. The results strongly support the future application of this technique for fundamental issues in the biomedical field.