In this section
@inproceedings{Wang:2025,
author = {Wang, S and Nan, Y and Xing, X and Fang, Y and Walsh, SLF and Yang, G},
pages = {4443--4454},
title = {A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Sets},
year = {2025}
}
TY - CPAPER
AB - Accurate segmentation of airways in LowResolution CT (LRCT) scans is vital for diagnostics in scenarios such as reduced radiation exposure, emergency response, or limited resources. Yet manual annotation is labor-intensive and prone to variability, while existing automated methods often fail to capture small airway branches in lowerresolution 3D data. To address this, we introduce FuzzySR, a parallel framework that merges superresolution (SR) and segmentation. By concurrently producing high-resolution reconstructions and precise airway masks, it enhances anatomic fidelity and captures delicate bronchi. FuzzySR employs a deep fuzzy set mechanism, leveraging learnable t-distribution and triangular membership functions via cross-attention. Through parameters μ, σ, and df, it preserves uncertain features and mitigates boundary noise. Extensive evaluations on lung cancer, COVID-19, and pulmonary fibrosis datasets confirm FuzzySR’s superior segmentation accuracy on LRCT, surpassing even high-resolution baselines. By uniting fuzzy-logic-driven uncertainty handling with SR-based resolution enhancement, FuzzySR effectively bridges the gap for robust airway delineation from LRCT data.
AU - Wang,S
AU - Nan,Y
AU - Xing,X
AU - Fang,Y
AU - Walsh,SLF
AU - Yang,G
EP - 4454
PY - 2025///
SP - 4443
TI - A Parallel Network for LRCT Segmentation and Uncertainty Mitigation with Fuzzy Sets
ER -
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