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DAVIDSTUTZ

Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz
21thMARCH2017

READING

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, O. Ronneberger. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. MICCAI, 2016.

Cicek et al. present a 3D convolutional network architecture for volumetric segmentation in the biomedical domain from sparse annotations. In particular, they propose the architecture shown in Figure 1, closely related to earlier work [1]. The network combines a decode stage, consisting of several convolutional and pooling layers, with a decoder part consisting of a series of convolutional and up-convolutional layers. Key to their model is that the loss function disregards voxels labeled as "unlabeled". Therefore, the network can be trained using volumes where only a few slices are densely labeled. They further utilize heavy data augmentation including rotations, gray value augmentation and smooth deformations.

Figure 1 (click to enlarge): Illustration of the network architecture.

  • [1] O. Ronneberger, P. Fischer, T. Brox. U-net: Convolutional networks for biomedical image segmentation. MICCAI, 2015.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or using the following platforms: