High-resolution Histology Images Research Articles

DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples

Background and objectiveThe use of several stains during histology sample preparation can be useful for fusing complementary information about different tissue structures. It reveals distinct tissue properties that combined may be useful for grading, classification, or 3-D reconstruction. Nevertheless, since the slide preparation is different for each stain and the procedure uses consecutive slices, the tissue undergoes complex and possibly large deformations. Therefore, a nonrigid registration is required before further processing. The nonrigid registration of differently stained histology images is a challenging task because: (i) the registration must be fully automatic, (ii) the histology images are extremely high-resolution, (iii) the registration should be as fast as possible, (iv) there are significant differences in the tissue appearance, and (v) there are not many unique features due to a repetitive texture. MethodsIn this article, we propose a deep learning-based solution to the histology registration. We describe a registration framework dedicated to high-resolution histology images that can perform the registration in real-time. The framework consists of an automatic background segmentation, iterative initial rotation search and learning-based affine/nonrigid registration. ResultsWe evaluate our approach using an open dataset provided for the Automatic Non-rigid Histological Image Registration (ANHIR) challenge organized jointly with the IEEE ISBI 2019 conference. We compare our solution to the challenge participants using a server-side evaluation tool provided by the challenge organizers. Following the challenge evaluation criteria, we use the target registration error (TRE) as the evaluation metric. Our algorithm provides registration accuracy close to the best scoring teams (median rTRE 0.19% of the image diagonal) while being significantly faster (the average registration time is about 2 seconds). ConclusionsThe proposed framework provides results, in terms of the TRE, comparable to the best-performing state-of-the-art methods. However, it is significantly faster, thus potentially more useful in clinical practice where a large number of histology images are being processed. The proposed method is of particular interest to researchers requiring an accurate, real-time, nonrigid registration of high-resolution histology images for whom the processing time of traditional, iterative methods in unacceptable. We provide free access to the software implementation of the method, including training and inference code, as well as pretrained models. Since the ANHIR dataset is open, this makes the results fully and easily reproducible.

Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics

Lately it has become possible to analyze transcriptomic profiles in tissue sections with retained cellular context. We aimed to explore synovial biopsies from rheumatoid arthritis (RA) and spondyloarthritis (SpA) patients, using Spatial Transcriptomics (ST) as a proof of principle approach for unbiased mRNA studies at the site of inflammation in these chronic inflammatory diseases. Synovial tissue biopsies from affected joints were studied with ST. The transcriptome data was subjected to differential gene expression analysis (DEA), pathway analysis, immune cell type identification using Xcell analysis and validation with immunohistochemistry (IHC). The ST technology allows selective analyses on areas of interest, thus we analyzed morphologically distinct areas of mononuclear cell infiltrates. The top differentially expressed genes revealed an adaptive immune response profile and T-B cell interactions in RA, while in SpA, the profiles implicate functions associated with tissue repair. With spatially resolved gene expression data, overlaid on high-resolution histological images, we digitally portrayed pre-selected cell types in silico. The RA displayed an overrepresentation of central memory T cells, while in SpA effector memory T cells were most prominent. Consequently, ST allows for deeper understanding of cellular mechanisms and diversity in tissues from chronic inflammatory diseases.

Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slides

Deep learning-based methods, such as the sliding window approach for cropped-image classification and heuristic aggregation for whole-slide inference, for analyzing histological patterns in high-resolution microscopy images have shown promising results. These approaches, however, require a laborious annotation process and are fragmented. To evaluate a novel deep learning method that uses tissue-level annotations for high-resolution histological image analysis for Barrett esophagus (BE) and esophageal adenocarcinoma detection. This diagnostic study collected deidentified high-resolution histological images (N = 379) for training a new model composed of a convolutional neural network and a grid-based attention network. Histological images of patients who underwent endoscopic esophagus and gastroesophageal junction mucosal biopsy between January 1, 2016, and December 31, 2018, at Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire) were collected. The model was evaluated on an independent testing set of 123 histological images with 4 classes: normal, BE-no-dysplasia, BE-with-dysplasia, and adenocarcinoma. Performance of this model was measured and compared with that of the current state-of-the-art sliding window approach using the following standard machine learning metrics: accuracy, recall, precision, and F1 score. Of the independent testing set of 123 histological images, 30 (24.4%) were in the BE-no-dysplasia class, 14 (11.4%) in the BE-with-dysplasia class, 21 (17.1%) in the adenocarcinoma class, and 58 (47.2%) in the normal class. Classification accuracies of the proposed model were 0.85 (95% CI, 0.81-0.90) for the BE-no-dysplasia class, 0.89 (95% CI, 0.84-0.92) for the BE-with-dysplasia class, and 0.88 (95% CI, 0.84-0.92) for the adenocarcinoma class. The proposed model achieved a mean accuracy of 0.83 (95% CI, 0.80-0.86) and marginally outperformed the sliding window approach on the same testing set. The F1 scores of the attention-based model were at least 8% higher for each class compared with the sliding window approach: 0.68 (95% CI, 0.61-0.75) vs 0.61 (95% CI, 0.53-0.68) for the normal class, 0.72 (95% CI, 0.63-0.80) vs 0.58 (95% CI, 0.45-0.69) for the BE-no-dysplasia class, 0.30 (95% CI, 0.11-0.48) vs 0.22 (95% CI, 0.11-0.33) for the BE-with-dysplasia class, and 0.67 (95% CI, 0.54-0.77) vs 0.58 (95% CI, 0.44-0.70) for the adenocarcinoma class. However, this outperformance was not statistically significant. Results of this study suggest that the proposed attention-based deep neural network framework for BE and esophageal adenocarcinoma detection is important because it is based solely on tissue-level annotations, unlike existing methods that are based on regions of interest. This new model is expected to open avenues for applying deep learning to digital pathology.

HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network

Among the predominant cancers, breast cancer is one of the main causes of cancer deaths impacting women worldwide. However, breast cancer classification is challenging due to numerous morphological and textural variations that appeared in intra-class images. Also, the direct processing of high resolution histological images is uneconomical in terms of GPU memory. In the present study, we have proposed a new approach for breast histopathological image classification that uses a deep convolution neural network (CNN) with wavelet decomposed images. The original microscopic image patches of 2048 × 1536 × 3 pixels are decomposed into 512 × 384 × 3 using 2-level Haar wavelet and subsequently used in proposed CNN model. The image decomposition step considerably reduces convolution time in deep CNNs and computational resources, without any performance downgrade. The CNN model extracts the deep features from Haar wavelet decomposed images and incorporates multi-scale discriminant features for precise prognostication of class labels. This paper also solves the demand for massive histopathology dataset by means of transfer learning and data augmentation techniques. The efficacy of proposed approach is corroborated on two publicly available breast histology datasets-(a) one provided as a part of international conference on image analysis and recognition (ICIAR 2018) grand challenge and (b) on BreakHis data. On the ICIAR 2018 validation data, our model showed an accuracy of 98.2% for both 4-class and 2-class recognition. Further, on hidden test data of the ICIAR 2018, we achieved an accuracy of 91%, outperforming existing state-of-the-art results significantly. Besides, on BreakHis dataset, the model achieved competing performance with 96.85% multi-class accuracy.

73-OR: Development of SteadiFlow Technology, an Advanced Insulin Delivery Cannula Featuring Extended Duration of Use, More Reliable Insulin Delivery, and Reduced Inflammatory Tissue Response

There is great clinical need for an insulin infusion set that produces reliable and consistent insulin absorption (PK) from dose to dose and functions reliably for 7 or more days. Capillary Biomedical, Inc. is developing SteadiFlow technology which features a soft yet kink-proof cannula with 1 distal orifice and 3 additional orifices distributed over the final 6 mm of the distal end of the cannula. We compared the performance of the SteadiSet to commercial insulin infusion sets with a Teflon cannula in large ambulatory swine. Investigational infusion sets featuring SteadiFlow technology and commercial Teflon infusion set were inserted into the subcutaneous tissue of swine’s abdomen every other day for 14 days. Insulin lispro (U-5) was infused through the catheters using the same basal/bolus pattern. On day # 14, a 70 ul bolus of insulin/x-ray contrast agent was infused through each CSII catheter. The tissue surrounding each CSII was excised 5 minutes later, frozen, and imaged using a high-resolution micro-CT scanner. The tissue was then processed and stained to produce high-resolution histology images. Histology images revealed a significantly thinner and smaller area of inflammatory tissue around the SteadiSet cannulas compared with the commercial Teflon cannulas. Micro-CT images revealed a significantly larger surface area of insulin/x-ray contrast agent delivered into the adjacent tissue when infused through the SteadiSet infusion sets compared with the commercial infusion sets with a Teflon cannula. A glucose clamp PK/PD study performed in swine using SteadiSet infusion sets implanted for 6 days showed reliable performance and relatively consistent insulin absorption into the circulation. Human clinical trials are planned for early 2019 to evaluate SteadiFlow technology for reliability, glucose control performance, and PK/PD metrics in patients with type 1 diabetes. Disclosure J.I. Joseph: Advisory Panel; Self; CapillaryBiomedical, Inc., Thermalin Diabetes, Inc. Research Support; Self; Capillary Biomedical, inc., Thermalin Diabetes, Inc. Stock/Shareholder; Self; Capillary Biomedical, Inc. M.C. Torjman: Other Relationship; Self; Capillary Biomedical, Inc. C. Loeum: Stock/Shareholder; Self; Capillary Biomedical, Inc. G.J. Eisler: None. P.J. Strasma: Employee; Self; Capillary Biomedical, Inc. A. Khalf: None. Funding JDRF; Capillary Biomedical, Inc.

Multi-modal Image Fusion for Multispectral Super-resolution in Microscopy.

Spectral imaging is a ubiquitous tool in modern biochemistry. Despite acquiring dozens to thousands of spectral channels, existing technology cannot capture spectral images at the same spatial resolution as structural microscopy. Due to partial voluming and low light exposure, spectral images are often difficult to interpret and analyze. This highlights a need to upsample the low-resolution spectral image by using spatial information contained in the high-resolution image, thereby creating a fused representation with high specificity both spatially and spectrally. In this paper, we propose a framework for the fusion of co-registered structural and spectral microscopy images to create super-resolved representations of spectral images. As a first application, we super-resolve spectral images of retinal tissue imaged with confocal laser scanning microscopy, by using spatial information from structured illumination microscopy. Second, we super-resolve mass spectroscopic images of mouse brain tissue, by using spatial information from high-resolution histology images. We present a systematic validation of model assumptions crucial towards maintaining the original nature of spectra and the applicability of super-resolution. Goodness-of-fit for spectral predictions are evaluated through functional R 2 values, and the spatial quality of the super-resolved images are evaluated using normalized mutual information.

EHistology image and annotation data from the Kaufman Atlas of Mouse Development.

“The Atlas of Mouse Development” by Kaufman is a classic paper atlas that is the de facto standard for the definition of mouse embryo anatomy in the context of standard histological images. We have redigitized the original haematoxylin and eosin–stained tissue sections used for the book at high resolution and transferred the hand-drawn annotations to digital form. We have augmented the annotations with standard ontological assignments (EMAPA anatomy) and made the data freely available via an online viewer (eHistology) and from the University of Edinburgh DataShare archive. The dataset captures and preserves the definitive anatomical knowledge of the original atlas, provides a core image set for deeper community annotation and teaching, and delivers a unique high-quality set of high-resolution histological images through mammalian development for manual and automated analysis.

Quantitative validation of a nonlinear histology-MRI coregistration method using generalized Q-sampling imaging in complex human cortical white matter

Advanced diffusion MRI methods have recently been proposed for detection of pathologies such as traumatic axonal injury and chronic traumatic encephalopathy which commonly affect complex cortical brain regions. However, radiological-pathological correlations in human brain tissue that detail the relationship between the multi-component diffusion signal and underlying pathology are lacking. We present a nonlinear voxel based two dimensional coregistration method that is useful for matching diffusion signals to quantitative metrics of high resolution histological images. When validated in ex vivo human cortical tissue at a 250×250×500 μm spatial resolution, the method proved robust in correlations between generalized q-sampling imaging and histologically based white matter fiber orientations, with r=0.94 for the primary fiber direction and r=0.88 for secondary fiber direction in each voxel. Importantly, however, the correlation was substantially worse with reduced spatial resolution or with fiber orientations derived using a diffusion tensor model. Furthermore, we have detailed a quantitative histological metric of white matter fiber integrity termed power coherence capable of distinguishing architecturally complex but intact white matter from disrupted white matter regions. These methods may allow for more sensitive and specific radiological-pathological correlations of neurodegenerative diseases affecting complex gray and white matter.

Molecular histology of fixed tissues using imaging mass spectrometry (1050.4)

Imaging Mass Spectrometry (IMS) yields molecular information from tissue sections that compliment traditional histology. Using the technique, distributions of label‐free proteins, lipids and small metabolites can be mapped to regions corresponding to specific histopathology. While it is widely acknowledged that fresh‐frozen tissue is preferable for IMS studies, the majority of pathology labs employ some form of fixation which often crosslinks endogenous proteins and makes them un‐detectable to IMS. Protocols for IMS analyses of Formalin‐Fixed Paraffin‐Embedded (FFPE) tissue have been described and include antigen retrieval followed by on‐tissue digestion prior to the analysis. Prior IMS studies using these protocols have yielded very low imaging resolutions, on the order of 200‐300 micron/pixel, which do not compare very well with high resolution histology. New protocols based on a commercial piezo‐driven sprayer have been developed that yield higher spatial resolution for imaging FFPE tissues. Overlaying these ion images with high resolution histology images show more comparable resolution but the need for the additional liquid‐based digestion step hinders one from achieving the 10 micron/pixel spatial resolution that can be achieved from a dry preparation. However, a secondary benefit of the protocol is that it lends itself favorably to methods for identifying structurally significant proteins from the digest peptides.

Computerized quantification of bone tissue and marrow in stained microscopic images

Stained histological images assist physicians to identify different types of tissues or cells and their architectures. They can be applied on the diagnosis of various diseases and the assessment of treatment effects. Osteoporosis is an aging disease that reduces the density of bones and increases the risk of bone fracture. Literatures indicate that osteoporosis is associated with the ratio of trabecular bone tissues and bone marrow cells, and bones in osteoporosis patients consist of a significantly higher marrow fat content. Interactive segmentation of bone tissue and different types of bone marrow cells in high-resolution histological images, however, is a very tedious and labor-intensive process. The aim of this study is to develop an automatic algorithm to quantify the areas of different tissues such as the trabecular bones and yellow and red marrow cells. This image segmentation method consists of a series of mathematical morphological operation steps based on both the color and morphology features of tissues and was implemented in Matlab. The results obtained from the proposed method have been verified by comparing with those obtained interactively from an experienced histotechnician (Pearson correlation coefficient > 0.94, P < 0.001). The result suggests that the proposed algorithm can effectively assist physicians to quantify stained bone histological images.

INVESTIGATION OF DISEASE IN STORED ONIONS USING OPTICAL COHERENCE TOMOGRAPHY

Optical coherence tomography (OCT) is an interferometric technique using near-infra-red light to probe the internal structures of semi-transparent samples. Images with a spatial resolution of 3-20 µm can be generated, mapping sub-surface structure to a depth of 1-2 mm, and allowing individual cells to be resolved. Onion (Allium cepa L.) tissue has been investigated using a Thorlabs OCT system based on a broadband 930 nm source. The specified depth resolution was ca. 7 µm and the lateral resolution ca. 10 µm. OCT real-time imaging to a depth of about 0.5 mm was achieved in onion tissue. Acquired images showed the cell-level structure of healthy tissue from several cultivars of stored onion, which were contrasted with images of samples suffering from fungal neck rot (Botrytis allii) or bacterial soft rot (Pseudomonas spp.). The variation in cell structure between different sites in the onion bulb was evident, and clear differences were also seen between images obtained from healthy and diseased tissue. Furthermore, images of tissue affected by neck rot exhibited different features from those showing symptoms of bacterial rot. Initial results demonstrated that OCT can offer an adaptable, real-time tissue-mapping capability, through rapid acquisition of high-resolution histological images. Comparable images obtained using a conventional microscope would require samples to undergo a lengthy slide-mounting procedure. Used alongside other diagnostic methods, OCT will be a significant aid in improving the understanding of both histological and pathological changes in fruit and vegetable crops during growth, harvest and storage.

Molecular Histology of Tissue using Imaging Mass Spectrometry and Classification Analysis

IntroductionImaging mass spectrometry (IMS) applied to fresh frozen or formalin‐fixed tissues enables the visualization of tissue/cancer specific protein/peptide distribution without prior knowledge of the target compounds. Classification analyses of these ion images exhibit strong correlation to high resolution histological images.MethodSerial sections from a tissue microarray (TMA) with formalin‐fixed and paraffin‐embedded (FFPE) normal tissue samples (n=12), cancer samples (n=30) and cell lines (n=16) from different organ sites were analyzed by on‐tissue digestion followed by MALDI imaging and MS/MS analysis.Preliminary dataOn average, 210 high abundance peptides were observed in the mass range from m/z 600–4000. Analyses of serial sections performed on different days and two different instruments showed reproducible patterns. Spectra from various samples, when compared with high resolution histology, showed distinctive differences in peak patterns. For example, peptides detected at m/z 1650.0 and 2089.2 were at significantly higher expression in samples with leiomyosarcoma. Similarly, peptides at m/z 1506.7 and 1177.7 were also seen with significantly higher abundances in pancreas and astrozytoma, respectively.

Towards an interactive digital atlas system for exploring anatomical connectivity patterns in the entire rat brain

Event Abstract Back to Event Towards an interactive digital atlas system for exploring anatomical connectivity patterns in the entire rat brain Izabela M. Zakiewicz1*, Yvette C. Van Dongen1, Ivar A. Moene1, Trygve B. Leergaard1 and Jan G. Bjaalie1 1 University of Oslo, CMBN & IMB, Norway Detailed knowledge about neuroanatomical projections and patterns of connectivity are essential for understanding brain function under normal and pathological conditions. Over the last century a tremendous amount of publications have reported neuronal connection data from the rat brain, which is a prevailing model in neuroscience research. Despite a rapid proliferation of anatomical data, we are still far from a comprehensive understanding of the wiring patterns of the brain. Retrieval of relevant information from legacy data is typically hampered by format and space restrictions in journal publications, and lack of standardized data presentations. Thus, there is a need for improved access to data describing precise neural connections.To this end, we are developing database systems for organizing, sharing, and exploring neuronal connection data together with metadata describing the animal and experimental parameters required to reuse data. Building upon infrastructure and tools developed within the Rodent Brain Workbench portal (www.rbwb.org) we are currently developing a digital atlas system holding comprehensive series of high-resolution histological images from neural tracing experiments performed using standardized procedures. Recent progress in the project includes registration of more than 1200 images of coronal sections derived from six tract-tracing experiments in which a tracer was injected into stereotaxically defined body representations in the rat primary somatosensory cortex. The images are spatially organized according to assigned stereotaxic coordinates and can be inspected in an interactive web-based viewer tool. The distribution of tracer labeling has been manually evaluated and is communicated in a comprehensive table in which each region is directly linked to corresponding sections in the image archive.The presented atlas system provides efficient access to a large number of detailed data on neuronal connections, potentially useful for a wide range of purposes, such as data mining for re-analysis, planning of new experiments, or interpretation of functional data involving the primary somatosensory cortex. The current pilot system serves to demonstrate proof of concept of how rat brain connectivity can be systematically mapped and communicated. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Oral Presentation Topic: Digital atlasing Citation: Zakiewicz IM, Van Dongen YC, Moene IA, Leergaard TB and Bjaalie JG (2019). Towards an interactive digital atlas system for exploring anatomical connectivity patterns in the entire rat brain. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.013 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 May 2009; Published Online: 09 May 2019. * Correspondence: Izabela M Zakiewicz, University of Oslo, CMBN & IMB, Oslo, Norway, i.m.zakiewicz@medisin.uio.no Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Izabela M Zakiewicz Yvette C Van Dongen Ivar A Moene Trygve B Leergaard Jan G Bjaalie Google Izabela M Zakiewicz Yvette C Van Dongen Ivar A Moene Trygve B Leergaard Jan G Bjaalie Google Scholar Izabela M Zakiewicz Yvette C Van Dongen Ivar A Moene Trygve B Leergaard Jan G Bjaalie PubMed Izabela M Zakiewicz Yvette C Van Dongen Ivar A Moene Trygve B Leergaard Jan G Bjaalie Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Interactive atlas of the rat hippocampus integrating high-resolution histological images and boundary definitions

Event Abstract Back to Event Interactive atlas of the rat hippocampus integrating high-resolution histological images and boundary definitions Lisa J. Kjonigsen1*, Trygve B. Leergaard1, Ivar A. Moene1, Menno P. Witter2 and Jan G. Bjaalie1 1 University of Oslo, CMBN & IMB, Norway 2 NTNU, Kavli Institute-CBM, Norway The rat hippocampus is a highly complex brain region frequently studied with relation to learning and memory processes as well as Alzheimer's disease and epilepsy. The region consists of several subdivisions that are difficult to delineate anatomically. Published atlases of the rat brain typically lack the underlying histological criteria necessary to identify boundaries, and textbooks describing the region are often inadequately illustrated and difficult to relate to experimental data. Without an available resource providing overview of cytoarchitectonic features with criteria for delineating the hippocampal region, the comparison of experimental results is challenging. To amend this, we are currently developing a novel integrated atlas application where high-resolution images of histological sections are directly linked to expert-defined criteria for subdividing cell layers of the rat hippocampus formation, fasciola and associated parahippocampal regions. The high-resolution images are spatially organized and stored in a relational Oracle database system (Moene et al, 2007) together with corresponding metadata. The web-based atlas application, comprising the database framework with an interactive image viewer based on Zoomify® technology, allows interactive access to a comprehensive section image repository and a text archive containing boundary descriptions and a systematic ontology for the region. The image viewer technology provides rapid zooming and panning of very large images through the web as well as specific links from individual boundary drawings in the images to corresponding text defining the anatomical criteria used for their delineation. Recent progress in this project includes the release of complete image sets of annotated sagittal and horizontal sections in addition to the coronal. More functionality has been included to the image viewer and the general design and user interaction have been developed further. The bi-directionally linked text and images constitute an intuitive and applicable web-based environment for exploring the anatomical subdivisions of the rat hippocampal region.The hippocampus atlas application will be made available via The Rodent WorkBench (http://www.rbwb.org). Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Oral Presentation Topic: Digital atlasing Citation: Kjonigsen LJ, Leergaard TB, Moene IA, Witter MP and Bjaalie JG (2019). Interactive atlas of the rat hippocampus integrating high-resolution histological images and boundary definitions. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.014 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 May 2009; Published Online: 09 May 2019. * Correspondence: Lisa J Kjonigsen, University of Oslo, CMBN & IMB, Oslo, Norway, l.j.kjonigsen@medisin.uio.no Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lisa J Kjonigsen Trygve B Leergaard Ivar A Moene Menno P Witter Jan G Bjaalie Google Lisa J Kjonigsen Trygve B Leergaard Ivar A Moene Menno P Witter Jan G Bjaalie Google Scholar Lisa J Kjonigsen Trygve B Leergaard Ivar A Moene Menno P Witter Jan G Bjaalie PubMed Lisa J Kjonigsen Trygve B Leergaard Ivar A Moene Menno P Witter Jan G Bjaalie Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Hypertext atlas of fetal and neonatal pathology

Hypertext atlas of fetal and neonatal pathology is a free resource for pregraduate students of medicine, pathologists and other health professionals dealing with prenatal medicine. The atlas can be found at . The access is restricted to registered users. Concise texts summarize the gross and microscopic pathology, etiology, and clinical signs of both common and rare fetal and neonatal conditions. The texts are illustrated with over 300 images that are accompanied by short comments. The atlas offers histological pictures of high quality. Virtual microscope interface is used to access the high-resolution histological images. Fetal ultrasound video clips are included. Case studies integrate clinical history, prenatal ultrasonographic examination, gross pathology and histological features. The atlas is available in English (and Czech) and equipped with an active index. The atlas is suitable both for medical students and pathologists as a teaching and reference tool. The atlas is going to be further expanded while keeping the high quality of the images.

The interactive Web-based histology atlas system.

Molecular pathways and mechanisms underlying human cancer are frequently investigated in animal models. Studies to identify and dissect molecular pathways include the discovery of genes and their subsequent analysis in transgenic and 'knock-out' mice. A critical aspect in such investigations is the evaluation of organ integrity and histology upon the alteration or inactivation of specific genes. Results from such studies are usually published in scientific journals. However, due to print space and costs the display of large high quality images is limited. Furthermore, the printed media does not permit an easy comparison of histological images published in different journals and different years. The Internet provides a tool for the timely and inexpensive dissemination of scientific data to the research community. However, its potential for the analysis of histological images has not been explored. Here we present a web-based interactive histology atlas (http://histology.nih.gov) that permits the retrieval of annotated, high-resolution histology images via the Internet. This histology atlas also takes advantage of the interactive nature of the Internet to support the communication between different research groups. As an outline forum this atlas provides the framework to evaluate and understand cancer pathology, and to develop a consensus between veterinary and human pathologists.