scholarly journals Investigating and Assessing the Dermoepidermal Junction with Multiphoton Microscopy and Deep Learning

2019 ◽  
Author(s):  
Mikko J. Huttunen ◽  
Radu Hristu ◽  
Adrian Dumitru ◽  
Mariana Costache ◽  
Stefan G. Stanciu
Keyword(s):  
Deep Learning ◽  
Human Error ◽  
Ex Vivo ◽  
Multiphoton Microscopy ◽  
Skin Cancer Screening ◽  
Dermoepidermal Junction ◽  
Optical Signals ◽  
Skin Cancers ◽  
Histopathological Image

AbstractHistopathological image analysis performed by a trained expert is currently regarded as the gold-standard in the case of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton Microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo. Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained Convolutional Neural Networks. Our results suggest that Deep Learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.

scholarly journals Rectal Cancer Treatment Management: Deep-Learning Neural Network Based on Photoacoustic Microscopy Image Outperforms Histogram-Feature-Based Classification

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiandong Leng ◽  
Eghbal Amidi ◽  
Sitai Kou ◽  
Hassam Cheema ◽  
Ebunoluwa Otegbeye ◽  
...  
Keyword(s):  
Neural Network ◽  
Rectal Cancer ◽  
Deep Learning ◽  
Linear Models ◽  
Ex Vivo ◽  
Residual Tumor ◽  
Photoacoustic Microscopy ◽  
Feature Based ◽  
Histogram Feature

We have developed a novel photoacoustic microscopy/ultrasound (PAM/US) endoscope to image post-treatment rectal cancer for surgical management of residual tumor after radiation and chemotherapy. Paired with a deep-learning convolutional neural network (CNN), the PAM images accurately differentiated pathological complete responders (pCR) from incomplete responders. However, the role of CNNs compared with traditional histogram-feature based classifiers needs further exploration. In this work, we compare the performance of the CNN models to generalized linear models (GLM) across 24 ex vivo specimens and 10 in vivo patient examinations. First order statistical features were extracted from histograms of PAM and US images to train, validate and test GLM models, while PAM and US images were directly used to train, validate, and test CNN models. The PAM-CNN model performed superiorly with an AUC of 0.96 (95% CI: 0.95-0.98) compared to the best PAM-GLM model using kurtosis with an AUC of 0.82 (95% CI: 0.82-0.83). We also found that both CNN and GLMs derived from photoacoustic data outperformed those utilizing ultrasound alone. We conclude that deep-learning neural networks paired with photoacoustic images is the optimal analysis framework for determining presence of residual cancer in the treated human rectum.

scholarly journals Sealing of Immersion Deuterium Dioxide and Its Application to Signal Maintenance for Ex-Vivo and In-Vivo Multiphoton Microscopy Excited at the 1700-nm Window

2017 ◽  
Vol 9 (5) ◽  
pp. 1-8
Author(s):  
Hongji Liu ◽  
Yu Du ◽  
Xiao Peng ◽  
Xuechang Zhou ◽  
Ping Qiu ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Multiphoton Microscopy

scholarly journals Mast cells acquire MHCII from dendritic cells during skin inflammation

2017 ◽  
Vol 214 (12) ◽  
pp. 3791-3811 ◽  
Author(s):  
Jan Dudeck ◽  
Anna Medyukhina ◽  
Julia Fröbel ◽  
Carl-Magnus Svensson ◽  
Johanna Kotrba ◽  
...  
Keyword(s):  
Dendritic Cells ◽  
T Cell ◽  
Mast Cells ◽  
Ex Vivo ◽  
Multiphoton Microscopy ◽  
Skin Inflammation ◽  
Critical Periods ◽  
Reporter Mice ◽  
Antigen Presenting

Mast cells (MCs) and dendritic cells (DCs) are essential innate sentinels populating host-environment interfaces. Using longitudinal intravital multiphoton microscopy of DCGFP/MCRFP reporter mice, we herein provide in vivo evidence that migratory DCs execute targeted cell-to-cell interactions with stationary MCs before leaving the inflamed skin to draining lymph nodes. During initial stages of skin inflammation, DCs dynamically scan MCs, whereas at a later stage, long-lasting interactions predominate. These innate-to-innate synapse-like contacts ultimately culminate in DC-to-MC molecule transfers including major histocompatibility complex class II (MHCII) proteins enabling subsequent ex vivo priming of allogeneic T cells with a specific cytokine signature. The extent of MHCII transfer to MCs correlates with their T cell priming efficiency. Importantly, preventing the cross talk by preceding DC depletion decreases MC antigen presenting capacity and T cell–driven inflammation. Consequently, we identify an innate intercellular communication arming resident MCs with key DC functions that might contribute to the acute defense potential during critical periods of migration-based DC absence.

scholarly journals Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy

2021 ◽  
Vol 11 (5) ◽  
pp. 1979
Author(s):  
Stefania Guida ◽  
Federica Arginelli ◽  
Francesca Farnetani ◽  
Silvana Ciardo ◽  
Laura Bertoni ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Laser Scanning ◽  
Ex Vivo ◽  
Real Life ◽  
Laser Scanning Microscopy ◽  
Pathological Examination ◽  
Confocal Laser ◽  
Fluorescence Confocal Microscopy ◽  
Skin Cancers

Confocal laser scanning microscopy (CLSM) has been introduced in clinical settings as a tool enabling a quasi-histologic view of a given tissue, without performing a biopsy. It has been applied to many fields of medicine mainly to the skin and to the analysis of skin cancers for both in vivo and ex vivo CLSM. In vivo CLSM involves reflectance mode, which is based on refractive index of cell structures serving as endogenous chromophores, reaching a depth of exploration of 200 μm. It has been proven to increase the diagnostic accuracy of skin cancers, both melanoma and non-melanoma. While histopathologic examination is the gold standard for diagnosis, in vivo CLSM alone and in addition to dermoscopy, contributes to the reduction of the number of excised lesions to exclude a melanoma, and to improve margin recognition in lentigo maligna, enabling tissue sparing for excisions. Ex vivo CLSM can be performed in reflectance and fluorescent mode. Fluorescence confocal microscopy is applied for “real-time” pathological examination of freshly excised specimens for diagnostic purposes and for the evaluation of margin clearance after excision in Mohs surgery. Further prospective interventional studies using CLSM might contribute to increase the knowledge about its application, reproducing real-life settings.

scholarly journals Biopsy-free in vivo virtual histology of skin using deep learning

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jingxi Li ◽  
Jason Garfinkel ◽  
Xiaoran Zhang ◽  
Di Wu ◽  
Yijie Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Ex Vivo ◽  
Normal Skin ◽  
Ground Truth ◽  
Skin Neoplasms ◽  
Cellular Level ◽  
Label Free ◽  
Virtual Histology

AbstractAn invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.

scholarly journals Deep learning-based fetoscopic mosaicking for field-of-view expansion

2020 ◽  
Vol 15 (11) ◽  
pp. 1807-1816 ◽  
Author(s):  
Sophia Bano ◽  
Francisco Vasconcelos ◽  
Marcel Tella-Amo ◽  
George Dwyer ◽  
Caspar Gruijthuijsen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ex Vivo ◽  
Video Data ◽  
Field Of View ◽  
Small Subset ◽  
Data Generation ◽  
Minimally Invasive Surgical Procedure ◽  
Minimally Invasive Surgical ◽  
Deep Image

Abstract Purpose Fetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the blood flow in both fetuses. This procedure is particularly challenging due to the limited field of view, poor visibility, occasional bleeding, and poor image quality. Fetoscopic mosaicking can help in creating an image with the expanded field of view which could facilitate the clinicians during the TTTS procedure. Methods We propose a deep learning-based mosaicking framework for diverse fetoscopic videos captured from different settings such as simulation, phantoms, ex vivo, and in vivo environments. The proposed mosaicking framework extends an existing deep image homography model to handle video data by introducing the controlled data generation and consistent homography estimation modules. Training is performed on a small subset of fetoscopic images which are independent of the testing videos. Results We perform both quantitative and qualitative evaluations on 5 diverse fetoscopic videos (2400 frames) that captured different environments. To demonstrate the robustness of the proposed framework, a comparison is performed with the existing feature-based and deep image homography methods. Conclusion The proposed mosaicking framework outperformed existing methods and generated meaningful mosaic, while reducing the accumulated drift, even in the presence of visual challenges such as specular highlights, reflection, texture paucity, and low video resolution.

Effects of Tissue Dehydration on Optical Diffuse Reflectance and Transmittance in Ex Vivo Porcine Skin

2012 ◽  
Author(s):  
William C. Vogt ◽  
Christopher G. Rylander
Keyword(s):  
Water Distribution ◽  
Diffuse Reflectance ◽  
Soft Tissues ◽  
Ex Vivo ◽  
Optical Response ◽  
Tissue Water ◽  
Optical Signals ◽  
Optical Clearing ◽  
High Degree

Soft tissues are heterogeneous materials that may be considered mixtures of water, proteins, and cells. The high degree of mismatch in refractive index between these constituents causes tissues to be highly turbid media [1]. Mechanical optical clearing is a technique for reducing tissue scattering and improving light-based diagnostics and therapeutics. Mechanical optical clearing is performed using indentation to locally modify tissue optical response, and this effect has been shown to be reversible in vivo [2]. This effect is attributed to transient changes in tissue water distribution as a result of interstitial pore flow of water due to tissue compression. This leads to the hypothesis that tissue optical response is also correlated to the tissue’s state of hydration. The goal of this study was to investigate whether or not a difference in tissue water content produces a measurable difference in tissue optical response and to correlate that response with mechanical deformation. Both diffuse reflectance and transmittance were selected as extrinsic optical signals of interest.

scholarly journals Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning

2019 ◽  
Vol 11 (1) ◽  
pp. 186 ◽  
Author(s):  
Mikko J. Huttunen ◽  
Radu Hristu ◽  
Adrian Dumitru ◽  
Iustin Floroiu ◽  
Mariana Costache ◽  
...  
Keyword(s):  
Deep Learning ◽  
Multiphoton Microscopy ◽  
Automated Identification ◽  
Dermoepidermal Junction

The Role of Polyphenols in the Modulation of Plasma Non-Enzymatic Antioxidant Capacity (NEAC)

2012 ◽  
Vol 82 (3) ◽  
pp. 228-232 ◽  
Author(s):  
Mauro Serafini ◽  
Giuseppa Morabito
Keyword(s):  
Antioxidant Capacity ◽  
Dietary Intervention ◽  
Ex Vivo ◽  
The Body ◽  
Dietary Polyphenols ◽  
Enzymatic Antioxidant ◽  
Endogenous Antioxidant

Dietary polyphenols have been shown to scavenge free radicals, modulating cellular redox transcription factors in different in vitro and ex vivo models. Dietary intervention studies have shown that consumption of plant foods modulates plasma Non-Enzymatic Antioxidant Capacity (NEAC), a biomarker of the endogenous antioxidant network, in human subjects. However, the identification of the molecules responsible for this effect are yet to be obtained and evidences of an antioxidant in vivo action of polyphenols are conflicting. There is a clear discrepancy between polyphenols (PP) concentration in body fluids and the extent of increase of plasma NEAC. The low degree of absorption and the extensive metabolism of PP within the body have raised questions about their contribution to the endogenous antioxidant network. This work will discuss the role of polyphenols from galenic preparation, food extracts, and selected dietary sources as modulators of plasma NEAC in humans.

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