scholarly journals Representación de imágenes de histopatología utilizada en tareas de análisis automático: estado del arte

Revista Med ◽  
2014 ◽  
Vol 22 (2) ◽  
pp. 79 ◽  
Author(s):  
John Arevalo ◽  
Angel Cruz-Roa ◽  
Fabio A. González O
Keyword(s):  
Image Analysis ◽  
Image Representation ◽  
Digital Pathology ◽  
Virtual Microscopy ◽  
Automatic Analysis ◽  
Computer Assisted ◽  
Automatic Image Analysis ◽  
Histopathology Images ◽  
Image Enhancing ◽  
Assisted Diagnosis

<p>This paper presents a review of the state-of-the-art in histopathology image representation used in automatic image analysis tasks. Automatic analysis of histopathology images is important for building computer-assisted diagnosis tools, automatic image enhancing systems and virtual microscopy systems, among other applications. Histopathology images have a rich mix of visual patterns with particularities that make them difficult to analyze. The paper discusses these particularities, the acquisition process and the challenges found when doing automatic analysis. Second an overview of recent works and methods addressed to deal with visual content representation in different automatic image analysis tasks is presented. Third an overview of applications of image representation methods in several medical domains and tasks is presented. Finally, the paper concludes with current trends of automatic analysis of histopathology images like digital pathology.</p>

Image analysis in modern ophthalmology: from acquisition to computer assisted diagnosis and telemedicine

2012 ◽  
Author(s):  
Andrés G. Marrugo ◽  
María S. Millán ◽  
Gabriel Cristóbal ◽  
Salvador Gabarda ◽  
Michal Sorel ◽  
...  
Keyword(s):  
Image Analysis ◽  
Computer Assisted ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

Computer-assisted diagnosis during blue light cystoscopy using image analysis methods: Ahead of pathology?

2018 ◽  
Vol 17 (2) ◽  
pp. e1241
Author(s):  
M.C. Kriegmair ◽  
A. Hartmann ◽  
T. Todenhöfer ◽  
N. Ali ◽  
G. Hipp ◽  
...  
Keyword(s):  
Image Analysis ◽  
Blue Light ◽  
Computer Assisted ◽  
Computer Assisted Diagnosis ◽  
Analysis Methods ◽  
Assisted Diagnosis

scholarly journals The value of GATA6 immunohistochemistry and computer-assisted diagnosis to predict clinical outcome in advanced pancreatic cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Duan ◽  
Gun-Ho Jang ◽  
Robert C. Grant ◽  
Julie M. Wilson ◽  
Faiyaz Notta ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Open Source Software ◽  
Digital Pathology ◽  
Advanced Pancreatic Cancer ◽  
Molecular Data ◽  
Ductal Adenocarcinoma ◽  
Computer Assisted ◽  
Biopsy Specimens ◽  
Assisted Diagnosis

AbstractCombination chemotherapy, either modified FOLFIRINOX (mFFX) or gemcitabine–nabpaclitaxel, are used in the treatment of most patients with advanced pancreatic ductal adenocarcinoma (PDAC), yet robust biomarkers of outcome are currently lacking to guide regimen selection. Here, we tested GATA6 immunohistochemistry (IHC) as a putative biomarker in advanced PDAC. GATA6 is a transcription factor in normal pancreas development. Two pathologists, blinded to clinical and molecular data, independently assessed GATA6 IHC in biopsy specimens of 130 patients with advanced PDAC, in 2 distinct phases (without and with computer assistance using the open source software QuPath). Low GATA6 IHC expression was associated with shorter overall survival [median OS 6.2 months for patients with GATA6 low tumors vs. 11.5 months for patients with GATA6 high tumors, HR 1.66 (95% CI 1.15–2.40), P = 0.007]. Progression appears to be higher in GATA6-low tumors compared to GATA6-high tumors in patients treated with mFFX (P = 0.024) but not in patients treated with gemcitabine regimens. GATA6 IHC expression was significantly associated with molecular subtypes (P = 0.0003). Digital assistance markedly improved interrater concordance (Cohen’s kappa scores of 0.32 vs. 0.95). Our results provide strong evidence that GATA6 IHC can be used as a single biomarker in the clinic to predict clinical outcome in advanced PDAC, warranting further investigation in prospective clinical trials. These results provide the basis for an improved classification of PDAC and future biomarker design using digital pathology workflow.

Image Analysis in the RGB and HS Colour Planes for a Computer-Assisted Diagnosis of Cutaneous Pigmented Lesions

1998 ◽  
Vol 84 (1) ◽  
pp. 29-32 ◽  
Author(s):  
Stefano Tomatis ◽  
Aldo Bono ◽  
Cesare Bartoli ◽  
Gabrina Tragni ◽  
Bruno Farina ◽  
...  
Keyword(s):  
Image Analysis ◽  
Video Camera ◽  
Computer Assisted ◽  
Blue Color ◽  
Color Representation ◽  
Computer Assisted Diagnosis ◽  
Image Descriptors ◽  
Clinical Observations ◽  
Pigmented Lesions ◽  
Assisted Diagnosis

Aims and background A study was carried out to evaluate the effectiveness of image analysis performed by the two color representation models when a computer-assisted diagnosis of melanoma is involved. Methods Color images of 40 skin pigmented lesions, which included 12 melanomas, were acquired by a standard color RGB video camera and stored in a PC for off-line processing. Image analysis was performed in the red green and blue color representation model and using hue and saturation color components. To describe shape and color characteristics of each lesion, including area, roundness and color variegation, 16 parameters were derived from red, green, blue, hue and saturation color planes and tested as possible variables useful to differentiate melanomas from benign nevi. Results The test gave a result of significance for six of the 16 derived image descriptors. The general trend of our data was in agreement with clinical observations according to which melanoma is usually darker, more variegated and less round than a benign nevus, whereas lesion dimension of melanomas and benign lesions was not significantly different. Conclusions Our preliminary results suggested that image analysis performed on hue and saturation-derived and red green and blue-derived data could better discriminate melanoma from nevi than separately using the two color representation models.

scholarly journals A fast and effective detection framework for whole-slide histopathology image analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251521
Author(s):  
Jun Ruan ◽  
Zhikui Zhu ◽  
Chenchen Wu ◽  
Guanglu Ye ◽  
Jingfan Zhou ◽  
...  
Keyword(s):  
Image Analysis ◽  
Adaptive Sampling ◽  
Rapid Development ◽  
Digital Pathology ◽  
Linear Interpolation ◽  
Convergence Condition ◽  
Computer Assisted ◽  
Grand Challenge ◽  
Histopathology Image Analysis ◽  
Local Sampling

Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection.

scholarly journals Advances in digital pathology

2018 ◽  
Vol 1 (2) ◽  
pp. 33-39
Author(s):  
Evgin Goceri
Keyword(s):  
Digital Pathology ◽  
Computer Assisted ◽  
Cell Detection ◽  
Tissue Samples ◽  
Nuclei Segmentation ◽  
New Ideas ◽  
Assisted Diagnosis ◽  
Tissue Specimens ◽  
Validation Of Results

Characterization of cancer diseases and preparation of diagnostic reports after analyzing tissue specimens and several cell samples are provided by pathologists. One of the most successful strategies in pathology is to divide tumors into different subtypes and to adapt the treatment for each tumor. However, this approach has put a big burden on pathologists, who are reviewing tissue samples under the light of the microscope. Because, tumors have about 200 subtypes and pathologies are facing a growing demand for accurate and fast diagnosis and also patient safety. Therefore, digital pathology has been important and growing rapidly. Advances in computer technology such as computing power, faster networks and cheaper storage have enabled pathologists to manage images more easily than in the last decade. Novel pathology tools have a potential for automated and faster diagnosis and also better management of data. Moreover, it enables re-reducibility, validation of results, quality assurance and sharing of new ideas at anywhere and anytime. Advances in digital pathology have been reviewed in this paper. It seems that innovations in technologies will not only provide important improvements in pathology service, but also they will change healthcare and research fundamentally despite some challenges.   Keywords: Cell detection, computer assisted diagnosis, digital pathology, image analysis, nuclei segmentation, tissue classification.          

Biodegradable polyester neuroprostheses promote the reconnection of separated peripheral nerve stubs

1992 ◽  
Vol 50 (2) ◽  
pp. 1094-1095
Author(s):  
Beverly L. Giammara ◽  
Jennifer S. Stevenson ◽  
Peggy E. Yates ◽  
Robert H. Gunderson ◽  
Jacob S. Hanker
Keyword(s):  
Image Analysis ◽  
Basement Membrane ◽  
Periodic Acid ◽  
Light And Electron Microscopy ◽  
Computer Assisted ◽  
Type Iii Collagen ◽  
Image Analysis System ◽  
Biodegradable Polyester ◽  
Adult Rats ◽  
Type Iv

An 11mm length of sciatic nerve was removed from 10 anesthetized adult rats and replaced by a biodegradable polyester Vicryl™ mesh sleeve which was then injected with the basement membrane gel, Matrigel™. It was noted that leg sensation and movement were much improved after 30 to 45 days and upon sacrifice nerve reconnection was noted in all animals. Epoxy sections of the repaired nerves were compared with those of the excised segments by the use of a variation of the PAS reaction, the PATS reaction, developed in our laboratories for light and electron microscopy. This microwave-accelerated technique employs periodic acid, thiocarbohydrazide and silver methenamine. It stains basement membrane or Type IV collagen brown and type III collagen (reticulin), axons, Schwann cells, endoneurium and perineurium black. Epoxy sections of repaired and excised nerves were also compared by toluidine blue (tb) staining. Comparison of the sections of control and repaired nerves was done by computer-assisted microscopic image analysis using an Olympus CUE-2 Image Analysis System.

Computerised Image Analysis of Nuclear Enlargement Assay in HeLa Cells

1991 ◽  
Vol 19 (1) ◽  
pp. 41-47
Author(s):  
Renato Rizzi ◽  
Francesco Re ◽  
Enzo Chiesara
Keyword(s):  
Image Analysis ◽  
Hela Cells ◽  
Automatic Analysis ◽  
Nuclear Size ◽  
Computerised Image Analysis ◽  
Nuclear Enlargement

It has been observed that cells often respond to carcinogens by nuclear enlargement. For this reason, new morphometric approaches have been developed to evaluate cell modifications in pre-carcinogenesis assays. Morphometric computerised automatic analysis, with original software, was performed on HeLa cells treated with various compounds (hydroxyurea, dimethylnitrosamine, N-methyl- N’-nitro-nitrosoguanidine and cyclophosphamide) to evaluate nuclear size changes.

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