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Aghigh A, Cardot J, Mohammadi MS, Jargot G, Ibrahim H, Plante I, Légaré F. Accelerating whole-sample polarization-resolved second harmonic generation imaging in mammary gland tissue via generative adversarial networks. BIOMEDICAL OPTICS EXPRESS 2024; 15:5251-5271. [PMID: 39296390 PMCID: PMC11407270 DOI: 10.1364/boe.529779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 07/23/2024] [Accepted: 07/25/2024] [Indexed: 09/21/2024]
Abstract
Polarization second harmonic generation (P-SHG) imaging is a powerful technique for studying the structure and properties of biological and material samples. However, conventional whole-sample P-SHG imaging is time consuming and requires expensive equipment. This paper introduces a novel approach that significantly improves imaging resolution under conditions of reduced imaging time and resolution, utilizing enhanced super-resolution generative adversarial networks (ESRGAN) to upscale low-resolution images. We demonstrate that this innovative approach maintains high image quality and analytical accuracy, while reducing the imaging time by more than 95%. We also discuss the benefits of the proposed method for reducing laser-induced photodamage, lowering the cost of optical components, and increasing the accessibility and applicability of P-SHG imaging in various fields. Our work significantly advances whole-sample mammary gland P-SHG imaging and opens new possibilities for scientific discovery and innovation.
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Affiliation(s)
- Arash Aghigh
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Jysiane Cardot
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique, Laval, Québec, Canada
| | - Melika Saadat Mohammadi
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Gaëtan Jargot
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Heide Ibrahim
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Isabelle Plante
- Centre Armand-Frappier Santé Biotechnologie, Institut National de la Recherche Scientifique, Laval, Québec, Canada
| | - François Légaré
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
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2
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Otin S, Ávila FJ, Mallen V, Garcia-Martin E. Detecting Structural Changes in the Choroidal Layer of the Eye in Neurodegenerative Disease Patients through Optical Coherence Tomography Image Processing. Biomedicines 2023; 11:2986. [PMID: 38001986 PMCID: PMC10669633 DOI: 10.3390/biomedicines11112986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 10/31/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE To evaluate alterations of the choroid in patients with a neurodegenerative disease versus healthy controls, a custom algorithm based on superpixel segmentation was used. DESIGN A cross-sectional study was conducted on data obtained in a previous cohort study. SUBJECTS Swept-source optical coherence tomography (OCT) B-scan images obtained using a Triton (Topcon, Japan) device were compiled according to current OSCAR IB and APOSTEL OCT image quality criteria. Images were included from three cohorts: multiple sclerosis (MS) patients, Parkinson disease (PD) patients, and healthy subjects. Only patients with early-stage MS and PD were included. METHODS In total, 104 OCT B-scan images were processed using a custom superpixel segmentation (SpS) algorithm to detect boundary limits in the choroidal layer and the optical properties of the image. The algorithm groups pixels with similar structural properties to generate clusters with similar meaningful properties. MAIN OUTCOMES SpS selects and groups the superpixels in a segmented choroidal area, computing the choroidal optical image density (COID), measured as the standard mean gray level, and the total choroidal area (CA), measured as px2. RESULTS The CA and choroidal density (CD) were significantly reduced in the two neurodegenerative disease groups (higher in PD than in MS) versus the healthy subjects (p < 0.001); choroidal area was also significantly reduced in the MS group versus the healthy subjects. The COID increased significantly in the PD patients versus the MS patients and in the MS patients versus the healthy controls (p < 0.001). CONCLUSIONS The SpS algorithm detected choroidal tissue boundary limits and differences optical density in MS and PD patients versus healthy controls. The application of the SpS algorithm to OCT images potentially acts as a non-invasive biomarker for the early diagnosis of MS and PD.
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Affiliation(s)
- Sofia Otin
- Department of Applied Physics, University of Zaragoza, 50009 Zaragoza, Spain;
| | - Francisco J. Ávila
- Department of Applied Physics, University of Zaragoza, 50009 Zaragoza, Spain;
| | - Victor Mallen
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (V.M.); (E.G.-M.)
- Miguel Servet Ophthalmology Research Group (GIMSO), Aragon Health Research Institute (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain
| | - Elena Garcia-Martin
- Department of Ophthalmology, Miguel Servet University Hospital, 50009 Zaragoza, Spain; (V.M.); (E.G.-M.)
- Miguel Servet Ophthalmology Research Group (GIMSO), Aragon Health Research Institute (IIS Aragon), University of Zaragoza, 50009 Zaragoza, Spain
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3
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Aghigh A, Preston SEJ, Jargot G, Ibrahim H, Del Rincón SV, Légaré F. Nonlinear microscopy and deep learning classification for mammary gland microenvironment studies. BIOMEDICAL OPTICS EXPRESS 2023; 14:2181-2195. [PMID: 37206132 PMCID: PMC10191635 DOI: 10.1364/boe.487087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/26/2023] [Accepted: 03/29/2023] [Indexed: 05/21/2023]
Abstract
Tumors, their microenvironment, and the mechanisms by which collagen morphology changes throughout cancer progression have recently been a topic of interest. Second harmonic generation (SHG) and polarization second harmonic (P-SHG) microscopy are label-free, hallmark methods that can highlight this alteration in the extracellular matrix (ECM). This article uses automated sample scanning SHG and P-SHG microscopy to investigate ECM deposition associated with tumors residing in the mammary gland. We show two different analysis approaches using the acquired images to distinguish collagen fibrillar orientation changes in the ECM. Lastly, we apply a supervised deep-learning model to classify naïve and tumor-bearing mammary gland SHG images. We benchmark the trained model using transfer learning with the well-known MobileNetV2 architecture. By fine-tuning the different parameters of these models, we show a trained deep-learning model that suits such a small dataset with 73% accuracy.
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Affiliation(s)
- Arash Aghigh
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Samuel E. J. Preston
- Department of Experimental Medicine, Faculty of Medicine, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, Segal Cancer Centre, Lady Davis Institute and Jewish General Hospital, McGill University, Montreal, Quebec, Canada
| | - Gaëtan Jargot
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Heide Ibrahim
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
| | - Sonia V Del Rincón
- Department of Experimental Medicine, Faculty of Medicine, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, Segal Cancer Centre, Lady Davis Institute and Jewish General Hospital, McGill University, Montreal, Quebec, Canada
| | - François Légaré
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Varennes, Québec, Canada
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4
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Martínez-Ojeda RM, Mugnier LM, Artal P, Bueno JM. Blind deconvolution of second harmonic microscopy images of the living human eye. BIOMEDICAL OPTICS EXPRESS 2023; 14:2117-2128. [PMID: 37206134 PMCID: PMC10191662 DOI: 10.1364/boe.486989] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 05/21/2023]
Abstract
Second harmonic generation (SHG) imaging microscopy of thick biological tissues is affected by the presence of aberrations and scattering within the sample. Moreover, additional problems, such as uncontrolled movements, appear when imaging in-vivo. Deconvolution methods can be used to overcome these limitations under some conditions. In particular, we present here a technique based on a marginal blind deconvolution approach for improving SHG images obtained in vivo in the human eye (cornea and sclera). Different image quality metrics are used to quantify the attained improvement. Collagen fibers in both cornea and sclera are better visualized and their spatial distributions accurately assessed. This might be a useful tool to better discriminate between healthy and pathological tissues, especially those where changes in collagen distribution occur.
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Affiliation(s)
- Rosa M. Martínez-Ojeda
- Laboratorio de Óptica,
Instituto Universitario de Investigación en
Óptica y Nanofísica, Universidad de
Murcia, Campus de Espinardo (Ed. 34), 30100 Murcia, Spain
| | | | - Pablo Artal
- Laboratorio de Óptica,
Instituto Universitario de Investigación en
Óptica y Nanofísica, Universidad de
Murcia, Campus de Espinardo (Ed. 34), 30100 Murcia, Spain
| | - Juan M. Bueno
- Laboratorio de Óptica,
Instituto Universitario de Investigación en
Óptica y Nanofísica, Universidad de
Murcia, Campus de Espinardo (Ed. 34), 30100 Murcia, Spain
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5
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Aghigh A, Bancelin S, Rivard M, Pinsard M, Ibrahim H, Légaré F. Second harmonic generation microscopy: a powerful tool for bio-imaging. Biophys Rev 2023; 15:43-70. [PMID: 36909955 PMCID: PMC9995455 DOI: 10.1007/s12551-022-01041-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/21/2022] [Indexed: 01/20/2023] Open
Abstract
Second harmonic generation (SHG) microscopy is an important optical imaging technique in a variety of applications. This article describes the history and physical principles of SHG microscopy and its more advanced variants, as well as their strengths and weaknesses in biomedical applications. It also provides an overview of SHG and advanced SHG imaging in neuroscience and microtubule imaging and how these methods can aid in understanding microtubule formation, structuration, and involvement in neuronal function. Finally, we offer a perspective on the future of these methods and how technological advancements can help make SHG microscopy a more widely adopted imaging technique.
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Affiliation(s)
- Arash Aghigh
- Centre Énergie Matériaux Télécommunications, Institut National de La Recherche Scientifique, Varennes, QC Canada
| | | | - Maxime Rivard
- National Research Council Canada, Boucherville, QC Canada
| | - Maxime Pinsard
- Institut National de Recherche en Sciences Et Technologies Pour L’environnement Et L’agriculture, Paris, France
| | - Heide Ibrahim
- Centre Énergie Matériaux Télécommunications, Institut National de La Recherche Scientifique, Varennes, QC Canada
| | - François Légaré
- Centre Énergie Matériaux Télécommunications, Institut National de La Recherche Scientifique, Varennes, QC Canada
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Dental Age Estimation Using Multiphoton Microscopy: A Potential Tool for Forensic Science. BIOMED RESEARCH INTERNATIONAL 2022; 2022:3328818. [PMID: 35937389 PMCID: PMC9355766 DOI: 10.1155/2022/3328818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/09/2022] [Indexed: 11/17/2022]
Abstract
Normal aging affects the different structures of teeth, in particular, the dentine. These changes are useful in forensic disciplines as a tool for age estimation. Although multiphoton (MP) microscopy has been used to explore dental pieces, a relationship between age and MP response of the human dentine has not been proposed yet. The relationship between MP signals and natural dentine aging is investigated herein. An index of age (INAG) combining two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) images has been used to quantify these changes. The results show that the INAG significantly decreases with age. Moreover, peritubular dentine size and collagen internal properties are also modified with age. This information confirms the usefulness of this technique in forensic age estimation after disasters (natural or manmade) with a lack of comprehensive fingerprint database. Courts and other government authorities might also benefit from this tool when the official age of individuals under special circumstances is required for legal or medical reasons.
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7
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PSHG-TISS: A collection of polarization-resolved second harmonic generation microscopy images of fixed tissues. Sci Data 2022; 9:376. [PMID: 35780180 PMCID: PMC9250519 DOI: 10.1038/s41597-022-01477-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/13/2022] [Indexed: 11/23/2022] Open
Abstract
Second harmonic generation (SHG) microscopy is acknowledged as an established imaging technique capable to provide information on the collagen architecture in tissues that is highly valuable for the diagnostics of various pathologies. The polarization-resolved extension of SHG (PSHG) microscopy, together with associated image processing methods, retrieves extensive image sets under different input polarization settings, which are not fully exploited in clinical settings. To facilitate this, we introduce PSHG-TISS, a collection of PSHG images, accompanied by additional computationally generated images which can be used to complement the subjective qualitative analysis of SHG images. These latter have been calculated using the single-axis molecule model for collagen and provide 2D representations of different specific PSHG parameters known to account for the collagen structure and distribution. PSHG-TISS can aid refining existing PSHG image analysis methods, while also supporting the development of novel image processing and analysis methods capable to extract meaningful quantitative data from the raw PSHG image sets. PSHG-TISS can facilitate the breadth and widespread of PSHG applications in tissue analysis and diagnostics. Measurement(s) | Type I Collagen | Technology Type(s) | multi-photon laser scanning microscopy | Factor Type(s) | second order susceptibility tensor elements | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Environment | laboratory environment | Sample Characteristic - Location | Romania |
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Haghighat M, Browning L, Sirinukunwattana K, Malacrino S, Khalid Alham N, Colling R, Cui Y, Rakha E, Hamdy FC, Verrill C, Rittscher J. Automated quality assessment of large digitised histology cohorts by artificial intelligence. Sci Rep 2022; 12:5002. [PMID: 35322056 PMCID: PMC8943120 DOI: 10.1038/s41598-022-08351-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/03/2022] [Indexed: 02/07/2023] Open
Abstract
Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at \documentclass[12pt]{minimal}
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\begin{document}$$5\times$$\end{document}5× magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall ‘usability’ (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86–90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research.
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Affiliation(s)
- Maryam Haghighat
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK. .,CSIRO, Brisbane, QLD, Australia.
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Korsuk Sirinukunwattana
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
| | - Stefano Malacrino
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Nasullah Khalid Alham
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Ying Cui
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Emad Rakha
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Freddie C Hamdy
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jens Rittscher
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK. .,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.
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9
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LaViolette AK, Xu C. Shot noise limits on binary detection in multiphoton imaging. BIOMEDICAL OPTICS EXPRESS 2021; 12:7033-7048. [PMID: 34858697 PMCID: PMC8606150 DOI: 10.1364/boe.442442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/11/2021] [Accepted: 10/11/2021] [Indexed: 05/14/2023]
Abstract
Much of fluorescence-based microscopy involves detection of if an object is present or absent (i.e., binary detection). The imaging depth of three-dimensionally resolved imaging, such as multiphoton imaging, is fundamentally limited by out-of-focus background fluorescence, which when compared to the in-focus fluorescence makes detecting objects in the presence of noise difficult. Here, we use detection theory to present a statistical framework and metric to quantify the quality of an image when binary detection is of interest. Our treatment does not require acquired or reference images, and thus allows for a theoretical comparison of different imaging modalities and systems.
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10
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HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue. Sci Data 2020; 7:169. [PMID: 32503988 PMCID: PMC7275059 DOI: 10.1038/s41597-020-0500-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 04/21/2020] [Indexed: 11/09/2022] Open
Abstract
Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing steps that underlie the generation of the final mosaic, e.g. registration and blending of the contained image tiles. We introduce HISTOBREAST, an extensive collection of brightfield microscopy images that we collected in a principled manner under different acquisition conditions on Haematoxylin - Eosin (H&E) stained breast tissue. HISTOBREAST is comprised of neighbour image tiles and ensemble of mosaics composed from different combinations of the available image tiles, exhibiting progressively degraded quality levels. HISTOBREAST can be used to benchmark image processing and computer vision techniques with respect to their robustness to image modifications specific to brightfield microscopy of H&E stained tissues. Furthermore, HISTOBREAST can serve in the development of new image processing methods, with the purpose of ensuring robustness to typical image artefacts that raise interpretation problems for expert histopathologists and affect the results of computerized image analysis.
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11
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Huttunen MJ, Hristu R, Dumitru A, Floroiu I, Costache M, Stanciu SG. Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning. BIOMEDICAL OPTICS EXPRESS 2020; 11:186-199. [PMID: 32010509 PMCID: PMC6968761 DOI: 10.1364/boe.11.000186] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 10/24/2019] [Accepted: 11/05/2019] [Indexed: 05/05/2023]
Abstract
Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard for the diagnostics 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 fixed 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.
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Affiliation(s)
- Mikko J. Huttunen
- Photonics Laboratory, Physics Unit, Tampere University, Tampere, Finland
- These authors contributed equally to this work
| | - Radu Hristu
- Center for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania
- These authors contributed equally to this work
| | - Adrian Dumitru
- Department of Pathology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- These authors contributed equally to this work
| | - Iustin Floroiu
- Center for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania
- Faculty of Medical Engineering, Politehnica University of Bucharest, Bucharest, Romania
| | - Mariana Costache
- Department of Pathology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Stefan G. Stanciu
- Center for Microscopy-Microanalysis and Information Processing, Politehnica University of Bucharest, Bucharest, Romania
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Abstract
Two-photon (2P) microscopy is a powerful tool for imaging and exploring label-free biological tissues at high resolution. Although this type of microscopy has been demonstrated in ex vivo ocular tissues of both humans and animal models, imaging the human eye in vivo has always been challenging. This work presents a novel compact 2P microscope for non-contact imaging of the anterior part of the living human eye. The performance of the instrument was tested and the maximum permissible exposure to protect ocular tissues established. To the best of our knowledge, 2P images of the in vivo human cornea, the sclera and the trabecular meshwork are shown for the very first time. Acquired images are of enough quality to visualize collagen arrangement and morphological features of clinical interest. Future implementations of this technique may constitute a potential tool for early diagnosis of ocular diseases at submicron scale.
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13
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Hristu R, Eftimie LG, Stanciu SG, Tranca DE, Paun B, Sajin M, Stanciu GA. Quantitative second harmonic generation microscopy for the structural characterization of capsular collagen in thyroid neoplasms. BIOMEDICAL OPTICS EXPRESS 2018; 9:3923-3936. [PMID: 30338165 PMCID: PMC6191628 DOI: 10.1364/boe.9.003923] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 07/13/2018] [Accepted: 07/14/2018] [Indexed: 05/11/2023]
Abstract
Quantitative second harmonic generation microscopy was used to investigate collagen organization in the fibrillar capsules of human benign and malignant thyroid nodules. We demonstrate that the combination of texture analysis and second harmonic generation images of collagen can be used to differentiate between capsules surrounding the thyroid follicular adenoma and papillary carcinoma nodules. Our findings indicate that second harmonic generation microscopy can provide quantitative information about the collagenous capsule surrounding both the thyroid and thyroid nodules, which may complement traditional histopathological examination.
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Affiliation(s)
- Radu Hristu
- Center for Microcopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucharest, Romania
| | - Lucian G Eftimie
- Center for Microcopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucharest, Romania
- Central University Emergency Military Hospital, Pathology Department, 134 Calea Plevnei, 010825 Bucharest, Romania
- Carol Davila University of Medicine and Pharmacy, 37 Dionisie Lupu, 030167 Bucharest, Romania
| | - Stefan G Stanciu
- Center for Microcopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucharest, Romania
| | - Denis E Tranca
- Center for Microcopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucharest, Romania
| | - Bogdan Paun
- Faculty of Energetics, University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
- Currently with Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 26-28 George Baritiu St, 40002 Cluj-Napoca, Romania
| | - Maria Sajin
- Carol Davila University of Medicine and Pharmacy, 37 Dionisie Lupu, 030167 Bucharest, Romania
| | - George A Stanciu
- Center for Microcopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucharest, Romania
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