ArticlePloS one2013
An automated blur detection method for histological whole slide imaging.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A semi-automated observation approach to quantify mouse skeletal muscle differentiation using immunohistochemistry.Physiological reports · 2025Article
- [Artificial intelligence for pathology-how, where, and why?]Pathologie (Heidelberg, Germany) · 2024Article
- Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Computational pathology in ovarian cancer.Frontiers in oncology · 2022Review
- Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring.Transplantation · 2019Review
- Digital pathology and artificial intelligence.The Lancet. Oncology · 2019Review
- Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection.Journal of pathology informatics · 2019Article
- Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2018Article
- DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning.PloS one · 2018Article
- Recent developments in multiplexing techniques for immunohistochemistry.Expert review of molecular diagnostics · 2015Review
- SlideToolkit: an assistive toolset for the histological quantification of whole slide images.PloS one · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole slide scanners are novel devices that enable high-resolution imaging of an entire histological slide. Furthermore, the imaging is achieved in only a few minutes, which enables image rendering of large-scale studies involving multiple immunohistochemistry biomarkers. Although whole slide imaging has improved considerably, locally poor focusing causes blurred regions of the image. These artifacts may strongly affect the quality of subsequent analyses, making a slide review process mandatory. This tedious and time-consuming task requires the scanner operator to carefully assess the virtual slide and to manually select new focus points. We propose a statistical learning method that provides early image quality feedback and automatically identifies regions of the image that require additional focus points.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.