MIA is an open-source standalone deep learning application for microscopic image analysis

被引:3
|
作者
Koerber, Nils [1 ,2 ]
机构
[1] German Fed Inst Risk Assessment BfR, German Ctr Protect Lab Anim Bf3R, Berlin, Germany
[2] Robert Koch Inst, Ctr Artificial Intelligence Publ Hlth Res, Berlin, Germany
来源
CELL REPORTS METHODS | 2023年 / 3卷 / 07期
关键词
PLATFORM;
D O I
10.1016/j.crmeth.2023.100517
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
In recent years, the amount of data generated by imaging techniques has grown rapidly, along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, the Microscopic Image Analyzer (MIA) was developed. MIA combines a graphical user interface that obviates the need for pro-gramming skills with state-of-the-art deep-learning algorithms for segmentation, object detection, and clas-sification. It runs as a standalone, platform-independent application and uses open data formats, which are compatible with commonly used open-source software packages. The software provides a unified interface for easy image labeling, model training, and inference. Furthermore, the software was evaluated in a public competition and performed among the top three for all tested datasets.
引用
收藏
页数:11
相关论文
共 50 条
  • [31] ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing
    Thibeau-Sutre, Elina
    Diaz, Mauricio
    Hassanaly, Ravi
    Routier, Alexandre
    Dormont, Didier
    Colliot, Olivier
    Burgos, Ninon
    COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2022, 220
  • [32] Performing a Research Study Using Open-Source Deep Learning Models
    Kim, Hyungjin
    KOREAN JOURNAL OF RADIOLOGY, 2024, 25 (03) : 217 - 219
  • [33] CNTK: Microsoft's Open-Source Deep-Learning Toolkit
    Seide, Frank
    Agarwal, Amit
    KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, : 2135 - 2135
  • [34] MIA - A free and open source software for gray scale medical image analysis
    Wollny, Gert
    Kellman, Peter
    Ledesma-Carbayo, Maria-Jesus
    Skinner, Matthew M.
    Hublin, Jean-Jaques
    Hierl, Thomas
    SOURCE CODE FOR BIOLOGY AND MEDICINE, 2013, 8 (01):
  • [35] Fiji: an open-source platform for biological-image analysis
    Schindelin, Johannes
    Arganda-Carreras, Ignacio
    Frise, Erwin
    Kaynig, Verena
    Longair, Mark
    Pietzsch, Tobias
    Preibisch, Stephan
    Rueden, Curtis
    Saalfeld, Stephan
    Schmid, Benjamin
    Tinevez, Jean-Yves
    White, Daniel James
    Hartenstein, Volker
    Eliceiri, Kevin
    Tomancak, Pavel
    Cardona, Albert
    NATURE METHODS, 2012, 9 (07) : 676 - 682
  • [36] RETIMAT: an open-source software for retinal OCT image analysis
    Romero-Bascones, David
    Murueta-Goyena, Ane
    Wagner, Siegfried
    Struyven, Robbert
    Williamson, Dominic
    Keane, Pearse
    Barrenechea, Miatane
    Gabilondo, Inigo
    Ayala, Unai
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2023, 64 (08)
  • [37] Fiji: An open-source platform for biological-image analysis
    Schindelin J.
    Arganda-Carreras I.
    Frise E.
    Kaynig V.
    Longair M.
    Pietzsch T.
    Preibisch S.
    Rueden C.
    Saalfeld S.
    Schmid B.
    Tinevez J.-Y.
    White D.J.
    Hartenstein V.
    Eliceiri K.
    Tomancak P.
    Cardona A.
    Nature Methods, 2012, 9 (7) : 676 - 682
  • [38] A review of open-source machine learning algorithms for twitter text sentiment analysis and image classification
    Lynch, Conor
    Kehoe, Jacqueline
    O'leary, Christian
    Vakaloudis, Alex
    Smith, Gary
    Linger, Richrd
    2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2020,
  • [39] A Standalone Open-Source System for Optical Inspection of Printed Circuit Boards
    Ulger, Furkan
    Yuksel, Seniha Esen
    2019 SIGNAL PROCESSING ALGORITHMS, ARCHITECTURES, ARRANGEMENTS, AND APPLICATIONS (SPA 2019), 2019, : 105 - 110
  • [40] On the Performance of an Indoor Open-Source 5G Standalone Deployment
    Sahbafard, Arash
    Schmidt, Robert
    Kaltenberger, Florian
    Springer, Andreas
    Bernhard, Hans-Peter
    2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC, 2023,