U-net with optimal thresholding for small blob detection in medical images

Yanzhe Xu, Fei Gao, Teresa Wu, Kevin M. Bennett, Jennifer R. Charlton, Suryadipto Sarkar

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

2 Scopus citations

Abstract

Biomarkers identified from medical images are valuable for disease diagnosis and staging. Object detection and segmentation are important for improving the accuracy of biomarker identification. It can be challenging to detect objects, especially small objects or 'blobs,' from 3D images secondary to low image resolution, noise, and overlap. Traditional small blob filters generate significant false positives as noise components can be misidentified as regions of interest. Recent advancements in U-Net-a deep learning model-have yielded more effective denoising capabilities that make it more suitable for small blob detection. However, unless the intensity threshold is appropriately established, U-Net tends to 'under-segment To address under-segmentation, we propose a combination of U-Net paired with optimal threshold detection via Otsu's thresholding. Two sets of experiments have been performed to compare this approach with both Hessian-based blob detection and U-Net with standard thresholding. The first experiment evaluated 20 simulated 3D images-comprising gloms with a varied size distribution, and noise. The second experiment examined MR images from 11 mouse kidneys with the objective of detecting all glomeruli. Our results support the conclusion that the proposed U-Net with optimal thresholding outperforms the Hessian-based detection and U-Net with standard thresholding approaches.

Original languageEnglish (US)
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages1761-1767
Number of pages7
ISBN (Electronic)9781728103556
DOIs
StatePublished - Aug 2019
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: Aug 22 2019Aug 26 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period8/22/198/26/19

Keywords

  • Deep learning
  • Hessian analysis
  • Kidney glomeruli
  • Medical imaging
  • Small blob detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Xu, Y., Gao, F., Wu, T., Bennett, K. M., Charlton, J. R., & Sarkar, S. (2019). U-net with optimal thresholding for small blob detection in medical images. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (pp. 1761-1767). [8843234] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8843234