![]() ![]() This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All files are available from DeepFocus image databaset (DOI: ) and the source code is available from. Received: SeptemAccepted: SeptemPublished: October 25, 2018Ĭopyright: © 2018 Senaras et al. PLoS ONE 13(10):Įditor: Chung-Ming Lo, Taipei Medical University, TAIWAN DeepFocus has the potential to be integrated with whole slide scanners to automatically re-scan problematic areas, hence improving the overall image quality for pathologists and image analysis algorithms.Ĭitation: Senaras C, Niazi MKK, Lozanski G, Gurcan MN (2018) DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning. When trained and tested on two independent datasets, DeepFocus resulted in an average accuracy of 93.2% (± 9.6%), which is a 23.8% improvement over an existing method. DeepFocus was trained by using 16 different H&E and IHC-stained slides that were systematically scanned on nine different focal planes, generating 216,000 samples with varying amounts of blurriness. DeepFocus is built on TensorFlow, an open source library that exploits data flow graphs for efficient numerical computation. The aim of this study is to develop a deep learning based software called, DeepFocus, which can automatically detect and segment blurry areas in digital whole slide images to address these problems. Moreover, this process is both tedious, and time-consuming. These areas are typically identified by visual inspection, which leads to a subjective evaluation causing high intra- and inter-observer variability. Moreover, these artifacts hamper the performance of computerized image analysis systems. Unfortunately, whole slide scanners often produce images with out-of-focus/blurry areas that limit the amount of tissue available for a pathologist to make accurate diagnosis/prognosis. The development of whole slide scanners has revolutionized the field of digital pathology. ![]()
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