PapaMadeleine2022 / fundus-vessel-segmentation-tbme

In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.

#Retinal Vessel Segmentation Created by José Ignacio Orlando at Pladema Institute (Facultad de Ciencias Exactas, UNCPBA, Tandil, Argentina) and CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina), under the supervision of Matthew B. Blaschko (KU Leuven, Belgium).

##Introduction This code corresponds to our paper published at IEEE TBME 2016. It allows you to perform vessel segmentation in color fundus images.

##License Our code is released under the MIT Licence (refer to the LICENSE file for details).

##Citing If you find our code useful for your research, please cite:

@article{orlando2016discriminatively,
  title={A discriminatively trained fully connected Conditional Random Field model for blood vessel segmentation in fundus images},
  author={Orlando, Jos{\'e} Ignacio and Prokofyeva, Elena and Blaschko, Matthew},
  journal={Biomedical Engineering, IEEE Transactions on},
  year={2016},
  publisher={IEEE}
}

@incollection{orlando2014learning,
  title={Learning fully-connected CRFs for blood vessel segmentation in retinal images},
  author={Orlando, Jos{\'e} Ignacio and Blaschko, Matthew},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2014},
  pages={634--641},
  year={2014},
  publisher={Springer}
}

There are also some third party libraries included in our code. If you use it, please cite:

• Responses to 2D Gabor wavelets by Soares et al. J. V. Soares et al.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging, vol. 25, no. 9, 2006

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• Line detectors by Nguyen et al. U. T. Nguyen et al.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognition, vol. 46, no. 3, pp. 703–715, 2013.

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• Efficient inference in fully connected CRF by Krahenbul and Koltun (C++ implementation) P. Krahenbuhl and V. Koltun: Efficient inference in fully connected CRFs with Gaussian edge potentials. Advances in Neural Information Processing Systems, 2012, pp. 109–117. (if you use the MEX function that wraps this code please also cite our IEEE TMBE and MICCAI papers)

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• Graph-cut for local neighborhood based CRF inference Y. Boykov and V. Kolmogorov: An experimental comparison of mincut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124–1137, 2004.

##Contents

###Requirements

1. Set up MEX compiler according to your OS.
2. Compile the Fully Connected CRF wrapper doing:

mex ./CRF/CRF_1.0/fullyCRFwithGivenPairwises.cpp ./CRF/CRF_1.0/densecrf.cpp ./CRF/CRF_1.0/util.cpp 
mex ./CRF/CRF_1.0/pairwisePart.cpp ./CRF/CRF_1.0/densecrf.cpp ./CRF/CRF_1.0/util.cpp

###Using the code Check out the user_manual.pdf file!