Fuzzy Attention-based Border Rendering Network for Lung Organ Segmentation

TL;DR

This paper introduces the FABR network, a novel lung segmentation method that uses fuzzy logic and border rendering to improve accuracy and continuity in challenging CT images.

Contribution

The paper proposes a fuzzy attention-based border rendering network with a global-local cube-tree fusion module for improved lung organ segmentation.

Findings

Achieves significantly better performance on four challenging datasets.

Effectively handles false negatives/positives and discontinuities.

Focuses on border vulnerable points for enhanced segmentation accuracy.

Abstract

Automatic lung organ segmentation on CT images is crucial for lung disease diagnosis. However, the unlimited voxel values and class imbalance of lung organs can lead to false-negative/positive and leakage issues in advanced methods. Additionally, some slender lung organs are easily lost during the recycled down/up-sample procedure, e.g., bronchioles & arterioles, causing severe discontinuity issue. Inspired by these, this paper introduces an effective lung organ segmentation method called Fuzzy Attention-based Border Rendering (FABR) network. Since fuzzy logic can handle the uncertainty in feature extraction, hence the fusion of deep networks and fuzzy sets should be a viable solution for better performance. Meanwhile, unlike prior top-tier methods that operate on all regular dense points, our FABR depicts lung organ regions as cube-trees, focusing only on recycle-sampled border vulnerable points, rendering the severely discontinuous, false-negative/positive organ regions with a novel Global-Local Cube-tree Fusion (GLCF) module. All experimental results, on four challenging datasets of airway & artery, demonstrate that our method can achieve the favorable performance significantly.