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Abstract #3556

Unsupervised learning for Abdominal MRI Segmentation using 3D Attention W-Net

Dhanunjaya Mitta1, Soumick Chatterjee1,2,3, Oliver Speck2,4,5,6, and Andreas N├╝rnberger1,3
1Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany, 2Department of Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, 3Data & Knowledge Engineering Group, Otto von Guericke University, Magdeburg, Germany, 4Center for Behavioral Sciences, Magdeburg, Germany, 5German Center for Neurodegenerative Disease, Magdeburg, Germany, 6Leibniz Insitute for Neurobiology, Magdeburg, Germany

Image segmentation is a process of dividing an image into multiple coherent regions. Segmentation of biomedical images can assist diagnosis and decision making. Manual segmentation is time consuming and requires expert knowledge. One solution is to segment medical images by using deep neural networks, but traditional supervised approaches need a large amount of manually segmented training data. A possible solution for the above issues is unsupervised medical image segmentation using deep neural networks, which our work tries to solve with our proposed 3D Attention W-Net.

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