Adjacent Slice Feature Guided 2.5D Network for Pulmonary Nodule Segmentation

TL;DR

This paper introduces a 2.5D neural network that leverages adjacent slice features and multi-scale fusion to improve pulmonary nodule segmentation, balancing accuracy and computational efficiency.

Contribution

It proposes an adjacent slice feature guided 2.5D network with multi-scale fusion and edge-constrained loss, addressing spatial relation and edge accuracy issues in pulmonary nodule segmentation.

Findings

Outperforms existing methods in segmentation accuracy

Effectively captures spatial relations between slices

Improves edge delineation in segmentation results

Abstract

More and more attention has been paid to the segmentation of pulmonary nodules. Among the current methods based on deep learning, 3D segmentation methods directly input 3D images, which takes up a lot of memory and brings huge computation. However, most of the 2D segmentation methods with less parameters and calculation have the problem of lacking spatial relations between slices, resulting in poor segmentation performance. In order to solve these problems, we propose an adjacent slice feature guided 2.5D network. In this paper, we design an adjacent slice feature fusion model to introduce information from adjacent slices. To further improve the model performance, we construct a multi-scale fusion module to capture more context information, in addition, we design an edge-constrained loss function to optimize the segmentation results in the edge region. Fully experiments show that our method performs better than other existing methods in pulmonary nodule segmentation task.