A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation

2017/12/01 by Zhuotun Zhu, Zhu, Zhuotun, Yingda Xia +7
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Medical Image Segmentation Techniques

paper · pdf · doi:10.48550/arxiv.1712.00201

openalex publication_date 2017/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarse-to-fine framework to effectively and efficiently tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial infor- mation along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the current state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). On the NIH pancreas segmentation dataset, we outperform the previous best by an average of over 2%, and the worst case is improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Residual Learning for Image Recognition
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Spatial Aggregation of Holistically-Nested Networks for Automated\n Pancreas Segmentation
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
• Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
• Fully Convolutional Networks for Semantic Segmentation

Related

• V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
• Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions
• Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation
• DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution
• Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey