Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet

Abstract

Pancreas segmentation in medical imaging data is of great significance for clinical pancreas diagnostics and treatment. However, the large population variations in the pancreas shape and volume cause enormous segmentation difficulties, even for state-of-the-art algorithms utilizing fully-convolutional neural networks (FCNs). Specifically, pancreas segmentation suffers from the loss of spatial information in 2D methods, and the high computational cost of 3D methods. To alleviate these problems, we propose a probabilistic-map-guided bi-directional recurrent UNet (PBR-UNet) architecture, which fuses intra-slice information and inter-slice probabilistic maps into a local 3D hybrid regularization scheme, which is followed by bi-directional recurrent network optimization. The PBR-UNet method consists of an initial estimation module for efficiently extracting pixel-level probabilistic maps and a primary segmentation module for propagating hybrid information through a 2.5D U-Net architecture. Specifically, local 3D information is inferred by combining an input image with the probabilistic maps of the adjacent slices into multichannel hybrid data, and then hierarchically aggregating the hybrid information of the entire segmentation network. Besides, a bi-directional recurrent optimization mechanism is developed to update the hybrid information in both the forward and the backward directions. This allows the proposed network to make full and optimal use of the local context information. Quantitative and qualitative evaluation was performed on the NIH Pancreas-CT dataset, and our proposed PBR-UNet method achieved better segmentation results with less computational cost compared to other state-of-the-art methods.

arXiv commentaccepted by Physics in Medicine & Biology

References in corpus (13)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Attention U-Net: Learning Where to Look for the Pancreas
• Fully Convolutional Networks for Semantic Segmentation
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• An application of cascaded 3D fully convolutional networks for medical image segmentation
• Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification
• Deep convolutional networks for pancreas segmentation in CT imaging
• Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
• H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
• Deep Learning for Medical Image Segmentation
• Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation
• Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation
• 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes

Cited by in corpus (3)

• Deep Learning for Pancreas Segmentation: a Systematic Review
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