A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
This work addresses the challenge of reliable pancreas segmentation for clinical applications, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of low segmentation accuracy for small organs like the pancreas in abdominal CT scans by proposing a fixed-point model that iteratively refines input regions, resulting in a more than 4% improvement in average Dice-Sørensen Coefficient over state-of-the-art methods.
Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans. However, the segmentation accuracy of some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupies a large fraction of the input volume. In this paper, we formulate this problem into a fixed-point model which uses a predicted segmentation mask to shrink the input region. This is motivated by the fact that a smaller input region often leads to more accurate segmentation. In the training process, we use the ground-truth annotation to generate accurate input regions and optimize network weights. On the testing stage, we fix the network parameters and update the segmentation results in an iterative manner. We evaluate our approach on the NIH pancreas segmentation dataset, and outperform the state-of-the-art by more than 4%, measured by the average Dice-Sørensen Coefficient (DSC). In addition, we report 62.43% DSC in the worst case, which guarantees the reliability of our approach in clinical applications.