Abstract
Whole heart segmentation significantly improves the diagnostic value of computed tomography images. Since manual segmentation is very time consuming, efforts have been made to automate this process, especially using deep learning methods such as convolutional neural networks (CNN). This paper considers how preprocessing of computed tomography with the Statistical Dominance Algorithm would affect the results of segmentation obtained with CNNs. Segmentation results were compared to original CTs and processed images. Compared to unprocessed data, improvements in segmentation accuracy were obtained after image processing. Using three-fold cross-validation, the average Dice similarity coefficient that was achieved was 0.811 for unprocessed images and 0.863 for processed images.
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