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A Multiple Covariance Approach for Cell Detection ofGram-Stained Smears Images

Microscope examination of Gram stained clinical specimens is used for aiding diagnosis of patients with infectious diseases. In high volumen pathology laboratories, this manual microscopy examination is considered time consuming and labour intensive. To that end, we propose a novel Gram stain image dataset, here called the UQ-SNP Gramstain Cell Detection (UQSNP_GCellDetect) dataset.


Dataset containing 8 Gram-stain slide images. Images are captured using a 2.5x objective (low-magnification).
Dataset containing 8 Gram-stain slide images. Images are captured using a 2.5x objective (low-magnification).


    
Dataset Information

The specimen slides for the dataset were prepared at the Sullivan Nicolaides Pathology laboratory, Australia. The slides were then scanned at the University of Queensland. All the patient data had been de-identified prior releasing the dataset.

-Description
  • 150 gram stain images.
  • Hardware: Zeiss motorised microscope & PixeLINK camera.
  • captured at x63 magnification.
  • A total of 8,129 cropped examples.
  • 586 leukocytes, 162 epithelial cells
  • 2,471 hand-labelled and 4,910 automatically extracted negative exemplars


-Evaluation Protocol

We use the leave-one-out cross validation Ten-fold cross validations.


Licence

The Gram Stain Dataset and associated data ('Licensed Material') are made available to the scientific community for non-commercial research purposes such as academic research, teaching, scientific publications or personal experimentation. Permission is granted to you (the 'Licensee') to use, copy and distribute the Licensed Material in accordance with the following terms and conditions:

  • Licensee must include a reference to the following publication in any published work that makes use of the Licensed Material:

A Multiple Covariance Approach for Cell Detection of Gram-Stained Smears Images
Matthew Crossman, Arnold Wiliem, Anthony Jennings and Brian C. Lovell
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015.

  • If the Licensee alters the content of the Licensed Material or creates any derivative work, Licensee must include in the altered Licensed Material or derivative work prominent notices to ensure that any recipients know that they are not receiving the original Licensed Material
  • Licensee may not use or distribute the Licensed Material or any derivative work for commercial purposes including but not limited to, licensing or selling the Licensed Material or using the Licensed Material for commercial gain
  • The Licensed Material is provided 'AS IS', without any expressed or implied warranties. The authors do not accept any responsibility for errors or omissions in the Licensed Material
  • This original licence notice must be retained in all copies or derivatives of the Licensed Material
  • All rights not expressly granted to the Licensee are reserved by the authors

Download

UQSNP_GCellDetect - images [download] (493 MB).
UQSNP_GCellDetect - groundtruth & validation sets [download] (326 KB).


Acknowledgements

This work has been funded by Sullivan Nicolaides Pathology, Australia and the Australian Research Council (ARC) Linkage Projects Grant LP130100230.


Citation

 M. Crossman, A. Wiliem, A. Jennings, B. C. Lovell "A Multiple Covariance Approach for Cell Detection of Gram-Stained Smears Images".  International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015. [pdf]