Comparisons Between Cross-Entropy Loss and Dice Loss
6.4 Future Work
6.4.3 Training of the Deep Neural Networks
In this thesis, the data material has been split into three data sets; training, validation, and test. K- fold cross validation is an alternative method that could be used, which might give a more robust and less biased estimation of the models [33]. Transfer learning is a technique that might be worth exploring. The method is based on using pre-trained DNNs, trained on large data sets. The pre-trained DNNs could further be trained on the LGE-CMR images.
Chapter 7
Conclusion
The objective of this thesis was to propose a method for automatic myocardial segmenta-tion in LGE-CMR images. The developed method was using an FCNN architecture and was trained end-to-end with a training set consisting of 2006 images and masks from 214 patients affected by MI.
Experiments with two different approaches were performed. Experiment one was to train the DNN with masks of the myocardium, and experiment two was using masks with the healthy myocardium and myocardial scar tissue to train the DNN.
The best model was obtained by binary segmentation. The model got a final result of a mean Dice score 0.705 with a standard deviation of 0.15, and a mean Jaccard index 0.560 with a standard deviation of 0.16. The model was evaluated using 244 images from 30 patients affected by myocardial infarction.
When evaluating the obtained results in this thesis, it is considered that the use of DNN for myocardial segmentation is a promising method worth exploring further.
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