NDER: A novel web application using annotated whole slide images for rapid improvements in human pattern recognition
| Autor: | Suzanne M. Dintzis, Rochelle L. Garcia, Daniel Glasser, Mara H. Rendi, Nicholas P. Reder, Jonathan Henriksen, Mark R. Kilgore |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Computer science Adaptive learning Health Informatics Context (language use) smartphone lcsh:Computer applications to medicine. Medical informatics Session (web analytics) Pathology and Forensic Medicine 03 medical and health sciences Annotation 0302 clinical medicine Technical Note lcsh:Pathology Web application business.industry pathology education whole-slide images Pattern recognition web application Pipeline (software) Computer Science Applications 030104 developmental biology Workflow 030220 oncology & carcinogenesis Pattern recognition (psychology) lcsh:R858-859.7 Artificial intelligence business Quality assurance lcsh:RB1-214 |
| Zdroj: | Journal of Pathology Informatics, Vol 7, Iss 1, Pp 31-31 (2016) Journal of Pathology Informatics |
| ISSN: | 2153-3539 |
| DOI: | 10.4103/2153-3539.186913 |
| Popis: | Context: Whole-slide images (WSIs) present a rich source of information for education, training, and quality assurance. However, they are often used in a fashion similar to glass slides rather than in novel ways that leverage the advantages of WSI. We have created a pipeline to transform annotated WSI into pattern recognition training, and quality assurance web application called novel diagnostic electronic resource (NDER). Aims: Create an efficient workflow for extracting annotated WSI for use by NDER, an attractive web application that provides high-throughput training. Materials and Methods: WSI were annotated by a resident and classified into five categories. Two methods of extracting images and creating image databases were compared. Extraction Method 1: Manual extraction of still images and validation of each image by four breast pathologists. Extraction Method 2: Validation of annotated regions on the WSI by a single experienced breast pathologist and automated extraction of still images tagged by diagnosis. The extracted still images were used by NDER. NDER briefly displays an image, requires users to classify the image after time has expired, then gives users immediate feedback. Results: The NDER workflow is efficient: annotation of a WSI requires 5 min and validation by an expert pathologist requires An additional one to 2 min. The pipeline is highly automated, with only annotation and validation requiring human input. NDER effectively displays hundreds of high-quality, high-resolution images and provides immediate feedback to users during a 30 min session. Conclusions: NDER efficiently uses annotated WSI to rapidly increase pattern recognition and evaluate for diagnostic proficiency. |
| Databáze: | OpenAIRE |
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