Deep Poincare Map For Robust Medical Image Segmentation

Published in arXiv preprint, 2017

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Precise segmentation is a prerequisite for an accurate quantification of the imaged objects. It is a very challenging task in many medical imaging applications due to relatively poor image quality and data scarcity. In this work, we present an innovative segmentation paradigm, named Deep Poincare Map (DPM), by coupling the dynamical system theory with a novel deep learning based approach. Firstly, we model the image segmentation process as a dynamical system, in which limit cycle models the boundary of the region of interest (ROI). Secondly, instead of segmenting the ROI directly, convolutional neural network is employed to predict the vector field of the dynamical system. Finally, the boundary of the ROI is identified using the Poincare map and the flow integration. We demonstrate that our segmentation model can be built using a very limited number of train- ing data. By cross-validation, we can achieve a mean Dice score of 94% compared to the manual delineation (ground truth) of the left ventricle ROI defined by clinical experts on a cardiac MRI dataset. Compared with other state-of-the-art methods, we can conclude that the proposed DPM method is adaptive, accurate and robust. It is straightforward to apply this method for other medical imaging applications.


  title={Deep Poincare Map For Robust Medical Image Segmentation},
  author={Mo, Yuanhan and Liu, Fangde and Zhang, Jingqing and Yang, Guang and He, Taigang and Guo, Yike},
  journal={arXiv preprint arXiv:1703.09200},