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Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network

  • Bin Kong
  • Yiqiang Zhan
  • Min Shin
  • Thomas Denny
  • Shaoting Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Accurate measurement of left ventricular volumes and Ejection Fraction from cine MRI is of paramount importance to the evaluation of cardiovascular functions, yet it usually requires laborious and tedious work of trained experts to interpret them. To facilitate this procedure, numerous computer aided diagnosis (CAD) methods and tools have been proposed, most of which focus on the left or right ventricle segmentation. However, the identification of ES and ED frames from cardiac sequences is largely ignored, which is a key procedure in the automated workflow. This seemingly easy task is quite challenging, due to the requirement of high accuracy (i.e., precisely identifying specific frames from a sequence) and subtle differences among consecutive frames. Recently, with the rapid growth of annotated data and the increasing computational power, deep learning methods have been widely exploited in medical image analysis. In this paper, we propose a novel deep learning architecture, named as temporal regression network (TempReg-Net), to accurately identify specific frames from MRI sequences, by integrating the Convolutional Neural Network (CNN) with the Recurrent Neural Network (RNN). Specifically, a CNN encodes the spatial information of a cardiac sequence, and a RNN decodes the temporal information. In addition, we design a new loss function in our network to constrain the structure of predicted labels, which further improves the performance. Our approach is extensively validated on thousands of cardiac sequences and the average difference is merely 0.4 frames, comparing favorably with previous systems.

Keywords

Recurrent Neural Network Convolutional Neural Network Left Ventricle Volume Ground Truth Label Convolutional Neural Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Bin Kong
    • 1
  • Yiqiang Zhan
    • 2
  • Min Shin
    • 1
  • Thomas Denny
    • 3
  • Shaoting Zhang
    • 1
  1. 1.Department of Computer ScienceUNC CharlotteCharlotteUSA
  2. 2.Siemens HealthcareMalvernUSA
  3. 3.MRI Research CenterAuburn UniversityAuburnUSA

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