Cascade Decoder: A Universal Decoding Method For Biomedical Image Segmentation

The Encoder-Decoder architecture is a main stream deep learning model for biomedical image segmentation. The encoder fully compresses the input and generates encoded features, and the decoder then produces dense predictions using encoded features. However, decoders are still under-explored in such architectures. In this paper, we comprehensively study the state-of-the-art Encoder-Decoder architectures, and propose a new universal decoder, called cascade decoder, to improve semantic segmentation accuracy. Our cascade decoder can be embedded into existing networks and trained altogether in an end-to-end fashion. The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders. We replace the decoders of state-of-the-art models with our cascade decoder for several challenging biomedical image segmentation tasks, and the considerable improvements achieved demonstrate the efficacy of our new decoding method.

[1]  Lin Yang,et al.  A new registration approach for dynamic analysis of calcium signals in organs , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[2]  Polina Golland,et al.  Interactive Whole-Heart Segmentation in Congenital Heart Disease , 2015, MICCAI.

[3]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[4]  Hao Chen,et al.  VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation , 2016, ArXiv.

[5]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[6]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Hao Chen,et al.  Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets , 2017, MICCAI.

[8]  Ronald M. Summers,et al.  DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation , 2015, MICCAI.

[9]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[10]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Tao Zhao,et al.  Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes , 2018, MICCAI.

[13]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[14]  Hao Chen,et al.  3D Fully Convolutional Networks for Intervertebral Disc Localization and Segmentation , 2016, MIAR.

[15]  Walter J. Scheirer,et al.  Neuron Segmentation Using Deep Complete Bipartite Networks , 2017, MICCAI.