Ultrasound segmentation of rat hearts using convolution neural networks

James D. Dormer, Rongrong Guo, Ming Shen, Rong Jiang, Mary B. Wagner, Baowei Fei

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

2 Scopus citations


Ultrasound is widely used for diagnosing cardiovascular diseases. However, estimates such as left ventricle volume currently require manual segmentation, which can be time consuming. In addition, cardiac ultrasound is often complicated by imaging artifacts such as shadowing and mirror images, making it difficult for simple intensity-based automated segmentation methods. In this work, we use convolutional neural networks (CNNs) to segment ultrasound images of rat hearts embedded in agar phantoms into four classes: Background, myocardium, left ventricle cavity, and right ventricle cavity. We also explore how the inclusion of a single diseased heart changes the results in a small dataset. We found an average overall segmentation accuracy of 70.0% ± 7.3% when combining the healthy and diseased data, compared to 72.4% ± 6.6% for just the healthy hearts. This work suggests that including diseased hearts with healthy hearts in training data could improve segmentation results, while testing a diseased heart with a model trained on healthy hearts can produce accurate segmentation results for some classes but not others. More data are needed in order to improve the accuracy of the CNN based segmentation.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationUltrasonic Imaging and Tomography
EditorsNeb Duric, Brett C. Byram
ISBN (Electronic)9781510616493
StatePublished - 2018
Externally publishedYes
EventMedical Imaging 2018: Ultrasonic Imaging and Tomography - Houston, United States
Duration: Feb 13 2018Feb 15 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2018: Ultrasonic Imaging and Tomography
Country/TerritoryUnited States


  • Cardiac ultrasound
  • Cardiovascular disease
  • Convolutional neural networks
  • Heart disease
  • Image segmentation
  • Myocardium segmentation
  • Ultrasound

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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