A Model-Driven Stack-Based Fully Convolutional Network for Pancreas Segmentation

3 Mar 2019 Hao Li Jun Li Xiaozhu Lin Xiaohua Qian

The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods. To address this problem, we present a novel model-driven stack-based fully convolutional network with a sliding window fusion algorithm for pancreas segmentation, termed MDS-Net... (read more)

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Methods used in the Paper


METHOD TYPE
Concatenated Skip Connection
Skip Connections
ReLU
Activation Functions
Max Pooling
Pooling Operations
Convolution
Convolutions
U-Net
Semantic Segmentation Models
Memory Network
Working Memory Models