Deep Poincare Map For Robust Medical Image Segmentation

03/27/2017
by   Yuanhan Mo, et al.
0

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 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.

READ FULL TEXT

page 2

page 7

page 8

research
06/06/2022

Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices

Deep learning techniques, particularly convolutional neural networks, ha...
research
12/09/2020

AIDE: Annotation-efficient deep learning for automatic medical image segmentation

Accurate image segmentation is crucial for medical imaging applications....
research
12/03/2018

Elastic Boundary Projection for 3D Medical Imaging Segmentation

We focus on an important yet challenging problem: using a 2D deep networ...
research
08/04/2021

Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging

Incorporating boundaries of the imaging object as a priori information t...
research
03/22/2021

Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation

In clinical practice, regions of interest in medical imaging often need ...
research
09/16/2018

Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network

Automating classification and segmentation process of abnormal regions i...
research
06/26/2016

Interactive Image Segmentation From A Feedback Control Perspective

Image segmentation is a fundamental problem in computational vision and ...

Please sign up or login with your details

Forgot password? Click here to reset