高级检索
当前位置: 首页 > 详情页

Reflecting topology consistency and abnormality via learnable attentions for airway labeling

文献详情

资源类型:
WOS体系:
Pubmed体系:

收录情况: ◇ SCIE

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
出处:
ISSN:

关键词: Airway anatomical labeling Structural prior Anomaly detection Transformer

摘要:
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:
语种:
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:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q2 SURGERY Q3 ENGINEERING, BIOMEDICAL
最新[2024]版:
Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q2 SURGERY Q3 ENGINEERING, BIOMEDICAL

影响因子: 最新[2024版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版] 出版后一年[2024版]

第一作者:
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):
APA:
MLA:

资源点击量:25910 今日访问量:0 总访问量:1514 更新日期:2025-07-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 首都医科大学附属北京同仁医院 技术支持:重庆聚合科技有限公司 地址:北京市东城区东交民巷1号(100730)