Literature DB >> 32844560

Autofocusing technologies for whole slide imaging and automated microscopy.

Zichao Bian1, Chengfei Guo1, Shaowei Jiang1, Jiakai Zhu1, Ruihai Wang1, Pengming Song2, Zibang Zhang1, Kazunori Hoshino1, Guoan Zheng1.   

Abstract

Whole slide imaging (WSI) has moved digital pathology closer to diagnostic practice in recent years. Due to the inherent tissue topography variability, accurate autofocusing remains a critical challenge for WSI and automated microscopy systems. The traditional focus map surveying method is limited in its ability to acquire a high degree of focus points while still maintaining high throughput. Real-time approaches decouple image acquisition from focusing, thus allowing for rapid scanning while maintaining continuous accurate focus. This work reviews the traditional focus map approach and discusses the choice of focus measure for focal plane determination. It also discusses various real-time autofocusing approaches including reflective-based triangulation, confocal pinhole detection, low-coherence interferometry, tilted sensor approach, independent dual sensor scanning, beam splitter array, phase detection, dual-LED illumination and deep-learning approaches. The technical concepts, merits and limitations of these methods are explained and compared to those of a traditional WSI system. This review may provide new insights for the development of high-throughput automated microscopy imaging systems that can be made broadly available and utilizable without loss of capacity.
© 2020 Wiley-VCH GmbH.

Keywords:  deep learning; digital pathology; focus map; focus quality; whole slide imaging

Mesh:

Year:  2020        PMID: 32844560     DOI: 10.1002/jbio.202000227

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  7 in total

1.  Deep learning-based single-shot autofocus method for digital microscopy.

Authors:  Jun Liao; Xu Chen; Ge Ding; Pei Dong; Hu Ye; Han Wang; Yongbing Zhang; Jianhua Yao
Journal:  Biomed Opt Express       Date:  2021-12-14       Impact factor: 3.732

2.  Review of bio-optical imaging systems with a high space-bandwidth product.

Authors:  Jongchan Park; David J Brady; Guoan Zheng; Lei Tian; Liang Gao
Journal:  Adv Photonics       Date:  2021-06-26

3.  Affordable artificial intelligence-based digital pathology for neglected tropical diseases: A proof-of-concept for the detection of soil-transmitted helminths and Schistosoma mansoni eggs in Kato-Katz stool thick smears.

Authors:  Peter Ward; Peter Dahlberg; Ole Lagatie; Joel Larsson; August Tynong; Johnny Vlaminck; Matthias Zumpe; Shaali Ame; Mio Ayana; Virak Khieu; Zeleke Mekonnen; Maurice Odiere; Tsegaye Yohannes; Sofie Van Hoecke; Bruno Levecke; Lieven J Stuyver
Journal:  PLoS Negl Trop Dis       Date:  2022-06-17

Review 4.  Progress on deep learning in digital pathology of breast cancer: a narrative review.

Authors:  Jingjin Zhu; Mei Liu; Xiru Li
Journal:  Gland Surg       Date:  2022-04

5.  Correction of out-of-focus microscopic images by deep learning.

Authors:  Chi Zhang; Hao Jiang; Weihuang Liu; Junyi Li; Shiming Tang; Mario Juhas; Yang Zhang
Journal:  Comput Struct Biotechnol J       Date:  2022-04-20       Impact factor: 6.155

6.  Optimization and Fabrication of Multi-Level Microchannels for Long-Term Imaging of Bacterial Growth and Expansion.

Authors:  Hsieh-Fu Tsai; Daniel W Carlson; Anzhelika Koldaeva; Simone Pigolotti; Amy Q Shen
Journal:  Micromachines (Basel)       Date:  2022-04-07       Impact factor: 3.523

Review 7.  High-throughput whole-slide scanning to enable large-scale data repository building.

Authors:  Mark D Zarella; Keysabelis Rivera Alvarez
Journal:  J Pathol       Date:  2022-06-08       Impact factor: 9.883

  7 in total

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