Automatic in vivo cell detection in MRI

Muhammad Jamal Afridi, Xiaoming Liu, Erik Shapiro, Arun Ross

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • 4 Citations

Abstract

Due to recent advances in cell-based therapies, non-invasive monitoring of in vivo cells in MRI is gaining enormous interest. However, to date, the monitoring and analysis process is conducted manually and is extremely tedious, especially in the clinical arena. Therefore, this paper proposes a novel computer vision-based learning approach that creates superpixel-based 3D models for candidate spots in MRI, extracts a novel set of superfern features, and utilizes a partition-based Bayesian classifier ensemble to distinguish spots from non-spots. Unlike traditional ferns that utilize pixel-based differences, superferns exploit superpixel averages in computing difference-based features despite the absence of any order in superpixel arrangement. To evaluate the proposed approach, we develop the first labeled database with a total of more than 16 thousand labels on five in vivo and four in vitro MRI scans. Experimental results show the superiority of our approach in comparison to the two most relevant baselines. To the best of our knowledge, this is the first study to utilize a learning-based methodology for in vivo cell detection in MRI.

LanguageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages391-399
Number of pages9
Volume9351
ISBN (Print)9783319245737
DOIs
StatePublished - 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/9/15

Profile

Magnetic resonance imaging
Cell
Monitoring
Classifier Ensemble
Bayesian Classifier
3D Model
Computer Vision
Computer vision
Therapy
Labels
Baseline
Arrangement
Classifiers
Pixel
Pixels
Partition
Methodology
Computing
Evaluate
Experimental Results

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Afridi, M. J., Liu, X., Shapiro, E., & Ross, A. (2015). Automatic in vivo cell detection in MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, pp. 391-399). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351). Springer Verlag. DOI: 10.1007/978-3-319-24574-4_47

Automatic in vivo cell detection in MRI. / Afridi, Muhammad Jamal; Liu, Xiaoming; Shapiro, Erik; Ross, Arun.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9351 Springer Verlag, 2015. p. 391-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Afridi, MJ, Liu, X, Shapiro, E & Ross, A 2015, Automatic in vivo cell detection in MRI. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9351, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, Springer Verlag, pp. 391-399, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, Germany, 10/5/15. DOI: 10.1007/978-3-319-24574-4_47
Afridi MJ, Liu X, Shapiro E, Ross A. Automatic in vivo cell detection in MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9351. Springer Verlag. 2015. p. 391-399. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-24574-4_47
Afridi, Muhammad Jamal ; Liu, Xiaoming ; Shapiro, Erik ; Ross, Arun. / Automatic in vivo cell detection in MRI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9351 Springer Verlag, 2015. pp. 391-399 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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