Authors:
Benjamin Bracke
and
Klaus Brinker
Affiliation:
Hamm-Lippstadt University of Applied Sciences, Marker Allee 76-78, 59063 Hamm, Germany
Keyword(s):
Medical Image Segmentation, Semantic Segmentation, Kidney Tumours Segmentation, U-Net, Deep Learning, Transfer Learning, Hyperparameter Optimization.
Abstract:
Semantic segmentation of kidney tumours in medical image data is an important step for diagnosis as well as in planning and monitoring of treatments. Morphological heterogeneity of kidneys and tumours in medical image data is a major challenge for automatic segmentation methods, therefore segmentations are typically performed manually by radiologists. In this paper, we use a state-of-the-art segmentation method based on the deep learning U-Net architecture to propose a segmentation algorithm for automatic semantic segmentation of kidneys and kidney tumours of 2D CT images. Therefore, we particularly focus on transfer learning of U-Net architectures and provide an experimental evaluation of different hyperparameters for data augmentation, various loss functions, U-Net encoders with varying complexity as well as different transfer learning strategies to increase the segmentation accuracy. We have used the results of the evaluation to fix the hyperparameters of our final segmentation al
gorithm, which has achieved a high segmentation accuracy for kidney pixels and a lower segmentation accuracy for tumor pixels.
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