Literature DB >> 32010529

Whole slide imaging system using deep learning-based automated focusing.

Tathagato Rai Dastidar1, Renu Ethirajan1.   

Abstract

The auto focusing system, which involves moving a microscope stage along a vertical axis to find an optimal focus position, is the chief component of an automated digital microscope. Current automated focusing algorithms, especially those deployed in cost effective microscopy systems, often cannot match the efficiency of a skilled human operator in keeping a sample in focus. This work presents an auto focusing system that utilises the recent advances in machine learning, namely deep convolutional neural networks (CNN). It improves upon prior work in this domain. The results of the focusing algorithm are demonstrated on an open data set. We describe the practical implementation of this method on a low cost digital microscope to create a whole slide imaging system (WSI). Results of a clinical study using this WSI system are presented. The study demonstrates the efficacy of this system in a practical scenario.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

Entities:  

Year:  2019        PMID: 32010529      PMCID: PMC6968754          DOI: 10.1364/BOE.379780

Source DB:  PubMed          Journal:  Biomed Opt Express        ISSN: 2156-7085            Impact factor:   3.732


  5 in total

1.  Autofocus evaluation for brightfield microscopy pathology.

Authors:  Rafael Redondo; Gloria Bueno; Juan Carlos Valdiviezo; Rodrigo Nava; Gabriel Cristóbal; Oscar Déniz; Marcial García-Rojo; Jesus Salido; Maria del Milagro Fernández; Juan Vidal; Boris Escalante-Ramírez
Journal:  J Biomed Opt       Date:  2012-03       Impact factor: 3.170

2.  An automated microscope for cytologic research a preliminary evaluation.

Authors:  J F Brenner; B S Dew; J B Horton; T King; P W Neurath; W D Selles
Journal:  J Histochem Cytochem       Date:  1976-01       Impact factor: 2.479

3.  Mitosis detection in breast cancer histology images with deep neural networks.

Authors:  Dan C Cireşan; Alessandro Giusti; Luca M Gambardella; Jürgen Schmidhuber
Journal:  Med Image Comput Comput Assist Interv       Date:  2013

4.  Transform- and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging.

Authors:  Shaowei Jiang; Jun Liao; Zichao Bian; Kaikai Guo; Yongbing Zhang; Guoan Zheng
Journal:  Biomed Opt Express       Date:  2018-03-08       Impact factor: 3.732

Review 5.  Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA.

Authors:  Esther Abels; Liron Pantanowitz
Journal:  J Pathol Inform       Date:  2017-05-15
  5 in total
  7 in total

Review 1.  Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects.

Authors:  Jan-Niklas Eckardt; Martin Bornhäuser; Karsten Wendt; Jan Moritz Middeke
Journal:  Blood Adv       Date:  2020-12-08

2.  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

3.  Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning.

Authors:  Chen Li; Mani Ratnam Rai; H Troy Ghashghaei; Alon Greenbaum
Journal:  Biomed Opt Express       Date:  2022-01-21       Impact factor: 3.732

4.  Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears.

Authors:  Jan-Niklas Eckardt; Jan Moritz Middeke; Karsten Wendt; Martin Bornhäuser; Sebastian Riechert; Tim Schmittmann; Anas Shekh Sulaiman; Michael Kramer; Katja Sockel; Frank Kroschinsky; Ulrich Schuler; Johannes Schetelig; Christoph Röllig; Christian Thiede
Journal:  Leukemia       Date:  2021-09-08       Impact factor: 11.528

5.  Deep learning identifies Acute Promyelocytic Leukemia in bone marrow smears.

Authors:  Karsten Wendt; Jan Moritz Middeke; Jan-Niklas Eckardt; Tim Schmittmann; Sebastian Riechert; Michael Kramer; Anas Shekh Sulaiman; Katja Sockel; Frank Kroschinsky; Johannes Schetelig; Lisa Wagenführ; Ulrich Schuler; Uwe Platzbecker; Christian Thiede; Friedrich Stölzel; Christoph Röllig; Martin Bornhäuser
Journal:  BMC Cancer       Date:  2022-02-22       Impact factor: 4.430

6.  Innovative Image Processing Method to Improve Autofocusing Accuracy.

Authors:  Chien-Sheng Liu; Ho-Da Tu
Journal:  Sensors (Basel)       Date:  2022-07-05       Impact factor: 3.847

7.  Automatic Segmentation of Novel Coronavirus Pneumonia Lesions in CT Images Utilizing Deep-Supervised Ensemble Learning Network.

Authors:  Yuanyuan Peng; Zixu Zhang; Hongbin Tu; Xiong Li
Journal:  Front Med (Lausanne)       Date:  2022-01-03
  7 in total

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