PurposeAccurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway labeling is challenging due to significant anatomical variations. Previous methods are prone to generate inconsistent predictions, hindering preoperative planning and intraoperative navigation. This paper aims to enhance topological consistency and improve the detection of abnormal airway branches.MethodsWe propose a transformer-based framework incorporating two modules: the soft subtree consistency (SSC) and the abnormal branch saliency (ABS). The SSC module constructs a soft subtree to capture clinically relevant topological relationships, allowing for flexible feature aggregation within and across subtrees. The ABS module facilitates interaction between node features and prototypes to distinguish abnormal branches, preventing the erroneous features aggregation between normal and abnormal nodes.ResultsEvaluated on a challenging dataset characterized by severe airway deformities, our method achieves superior performance compared to state-of-the-art approaches. Specifically, it attains an 83.7% subsegmental accuracy, along with a 3.1% increase in segmental subtree consistency, a 45.2% increase in abnormal branch recall. Notably, the method demonstrates robust performance in cases with airway deformities, ensuring consistent and accurate labeling.ConclusionThe enhanced topological consistency and robust identification of abnormal branches provided by our method offer an accurate and robust solution for airway labeling, with potential to improve the precision and safety of bronchoscopy procedures.
基金:
National Key R&D Program of China [2022ZD0212400]; Natural Science Foundation of China [62373243]; Science and Technology Commission of Shanghai Municipality, China [20DZ2220400]; Shanghai Municipal Science and Technology Major Project [2021SHZDZX0102]
语种:
外文
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2025]版:
大类|4 区医学
小类|4 区工程:生物医学4 区核医学4 区外科
最新[2025]版:
大类|4 区医学
小类|4 区工程:生物医学4 区核医学4 区外科
JCR分区:
出版当年[2023]版:
Q2RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGINGQ2SURGERYQ3ENGINEERING, BIOMEDICAL
最新[2024]版:
Q2RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGINGQ2SURGERYQ3ENGINEERING, BIOMEDICAL
第一作者机构:[1]Shanghai Jiao Tong Univ, Inst Med Robot, Shanghai, Peoples R China[2]Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China[3]Shanghai Jiao Tong Univ, Tongren Hosp, Shanghai Key Lab Flexible Med Robot, Shanghai, Peoples R China
通讯作者:
通讯机构:[1]Shanghai Jiao Tong Univ, Inst Med Robot, Shanghai, Peoples R China[2]Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Shanghai, Peoples R China[3]Shanghai Jiao Tong Univ, Tongren Hosp, Shanghai Key Lab Flexible Med Robot, Shanghai, Peoples R China
推荐引用方式(GB/T 7714):
Li Chenyu,Zhang Minghui,Zhang Chuyan,et al.Reflecting topology consistency and abnormality via learnable attentions for airway labeling[J].INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY.2025,doi:10.1007/s11548-025-03368-3.
APA:
Li, Chenyu,Zhang, Minghui,Zhang, Chuyan&Gu, Yun.(2025).Reflecting topology consistency and abnormality via learnable attentions for airway labeling.INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY,,
MLA:
Li, Chenyu,et al."Reflecting topology consistency and abnormality via learnable attentions for airway labeling".INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY .(2025)