Lymphocyte Annotator: CD3+ and CD8+ IHC Stained Patch Image Annotation Tool

Author(s):  
Muhammad Mohsin Zafar ◽  
Zunaira Rauf ◽  
Anabia Sohail ◽  
Asifullah Khan
2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
D. K. Iakovidis ◽  
T. Goudas ◽  
C. Smailis ◽  
I. Maglogiannis

Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.


Terminology ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 222-258 ◽  
Author(s):  
Pilar León-Araúz ◽  
Arianne Reimerink ◽  
Pamela Faber

Abstract Reutilization and interoperability are major issues in the fields of knowledge representation and extraction, as reflected in initiatives such as the Semantic Web and the Linked Open Data Cloud. This paper shows how terminological resources can be integrated and reused within different types of application. EcoLexicon is a multilingual terminological knowledge base (TKB) on environmental science that integrates conceptual, linguistic and visual information. It has led to the following by-products: (i) the EcoLexicon English Corpus; (ii) EcoLexiCAT, a terminology-enhanced translation tool; and (iii) Manzanilla, an image annotation tool. This paper explains EcoLexicon and its by-products, and shows how the latter exploit and enhance the data in the TKB.


2021 ◽  
Author(s):  
Tuomo Lahtinen ◽  
Hannu Turtiainen ◽  
Andrei Costin

2019 ◽  
Vol 6 ◽  
pp. 12-41
Author(s):  
Chris Dijkshoorn ◽  
Victor De Boer ◽  
Lora Aroyo ◽  
Guus Schreiber

With the increase of cultural heritage data published online, the usefulness of data in this open context hinges on the quality and diversity of descriptions of collection objects. In many cases, existing descriptions are not sufficient for retrieval and research tasks, resulting in the need for more specific annotations. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used to execute simple annotation tasks, identifying people with the required expertise might prove troublesome for more complex and domain-specific tasks. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is fourfold: 1) a nichesourcing methodology, 2) an annotation tool for experts, 3) validation of the methodology in three case studies and 4) a dataset including the obtained annotations. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high-quality annotations in a variety of domains. A user evaluation indicates the tool is suited and usable for domain-specific annotation tasks.


2018 ◽  
Vol ISASE2018 (0) ◽  
pp. 1-5 ◽  
Author(s):  
Shu ISAKA ◽  
Hiroharu KAWANAKA ◽  
V. B. Surya PRASATH ◽  
Bruce J. ARONOW ◽  
Shinji TSURUOKA

2021 ◽  
Author(s):  
Mustafa I. Jaber ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Christopher W. Szeto ◽  
Patricia Spilman ◽  
...  

ABSTRACTWell-annotated exemplars are an important prerequisite for supervised deep learning schemes. Unfortunately, generating these annotations is a cumbersome and laborious process, due to the large amount of time and effort needed. Here we present a deep-learning-based iterative digital pathology annotation tool that is both easy to use by pathologists and easy to integrate into machine vision systems. Our pathology image annotation tool greatly reduces annotation time from hours to a few minutes, while maintaining high fidelity with human-expert manual annotations. Here we demonstrate that our active learning tool can be used for a variety of pathology annotation tasks including masking tumor, stroma, and lymphocyte-rich regions, among others. This annotation automation system was validated on 90 unseen digital pathology images with tumor content from the CAMELYON16 database and it was found that pathologists’ gold standard masks were re-produced successfully using our tool. That is, an average of 2.7 positive selections (mouse clicks) and 8.0 negative selections (mouse clicks) were sufficient to generate tumor masks similar to pathologists’ gold standard in CAMELYON16 test WSIs. Furthermore, the developed image annotation tool has been used to build gold standard masks for hundreds of TCGA digital pathology images. This set was used to train a convolutional neural network for identification of tumor epithelium. The developed pan-cancer deep neural network was then tested on TCGA and internal data with comparable performance. The validated pathology image annotation tool described herein has the potential to be of great value in facilitating accurate, rapid pathological analysis of tumor biopsies.


2019 ◽  
Vol 28 (1) ◽  
pp. 69-77
Author(s):  
Marcin Bator ◽  
Maciej Pankiewicz

Images of natural scenes, like those relevant for agriculture, are characterised with a variety of forms of objects of interest and similarities between objects that one might want to discriminate. This introduces uncertainty to the analysis of such images. Requirements for an image annotation tool to be used in pattern recognition design for agriculture were discussed. A selection of open source annotating tools were presented. Advices how to use the software to handle uncertainty and missing functionalities were described.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 83852-83862 ◽  
Author(s):  
Dongyang Hou ◽  
Zelang Miao ◽  
Huaqiao Xing ◽  
Hao Wu

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