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DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning

Senaras, Caglar ; Niazi, M Khalid Khan ; Lozanski, Gerard ; Gurcan, Metin N Lo, Chung-Ming

PloS one, 2018-10, Vol.13 (10), p.e0205387 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning
Autor: Senaras, Caglar ; Niazi, M Khalid Khan ; Lozanski, Gerard ; Gurcan, Metin N
Lo, Chung-Ming
Assuntos: Algorithms ; Analysis ; Architectural engineering ; Area Under Curve ; Artificial intelligence ; Computer and Information Sciences ; Deep Learning ; Digital imaging ; Engineering and Technology ; Flow graphs ; Freeware ; Image analysis ; Image detection ; Image processing ; Image Processing, Computer-Assisted ; Image quality ; Informatics ; Inspection ; International conferences ; Machine learning ; Methods ; Neural networks ; Neural Networks, Computer ; Numerical analysis ; Optimization techniques ; Pathology ; Physical Sciences ; Quality ; Research and Analysis Methods ; ROC Curve ; Scanners
É parte de: PloS one, 2018-10, Vol.13 (10), p.e0205387
Notas: Competing Interests: The authors have declared that no competing interests exist.
These authors are joint senior authors on this work.
Descrição: The development of whole slide scanners has revolutionized the field of digital pathology. 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. Moreover, these artifacts hamper the performance of computerized image analysis systems. These areas are typically identified by visual inspection, which leads to a subjective evaluation causing high intra- and inter-observer variability. Moreover, this process is both tedious, and time-consuming. 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. DeepFocus is built on TensorFlow, an open source library that exploits data flow graphs for efficient numerical computation. 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. 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 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning

Senaras, Caglar ; Niazi, M Khalid Khan ; Lozanski, Gerard ; Gurcan, Metin N Lo, Chung-Ming

United States: Public Library of Science

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