  Segmentation of biomedical images can assist radiologists to make a better
diagnosis and take decisions faster by helping in the detection of
abnormalities, such as tumors. Manual or semi-automated segmentation, however,
can be a time-consuming task. Most deep learning based automated segmentation
methods are supervised and rely on manually segmented ground-truth. A possible
solution for the problem would be an unsupervised deep learning based approach
for automated segmentation, which this research work tries to address. We use a
W-Net architecture and modified it, such that it can be applied to 3D volumes.
In addition, to suppress noise in the segmentation we added attention gates to
the skip connections. The loss for the segmentation output was calculated using
soft N-Cuts and for the reconstruction output using SSIM. Conditional Random
Fields were used as a post-processing step to fine-tune the results. The
proposed method has shown promising results, with a dice coefficient of 0.88
for the liver segmentation compared against manual segmentation.
