Airway Segmentation Network for Enhanced Tubular Feature Extraction
School of Optical-electrical and Computer Engineering（光学电子与计算机工程学院）；University of Shanghai for Science and Technology（上海理工大学）；School of Information Technology（信息技术学院）；Jiangsu Open University（江苏开放大学）
在计算机断层扫描图像中手动标注气道区域是一项耗时且依赖专业知识的工作。因此，自动气道分割是实现快速支气管镜导航和支气管镜机器人系统临床部署的先决条件。尽管卷积神经网络方法在气道分割中已获得广泛关注，但气道独特的树状结构对常规卷积和可变形卷积提出了挑战，这些方法往往难以聚焦于精细的气道结构，导致分割结果出现遗漏和不连续。为解决这一问题，本研究提出了一种新颖的管状特征提取网络，命名为TfeNet。TfeNet引入了一种新颖的方向感知卷积操作，该操作首先应用空间旋转变换来调整线性卷积核的采样位置。然后，变形后的卷积核在三维空间中被表示为线段或多段线。此外，基于非对称卷积和残差连接策略设计了管状特征融合模块（TFFM），增强了网络对细微气道结构的关注。在一个公开数据集和两个用于气道分割挑战的数据集上进行的大量实验表明，与现有方法相比，所提出的TfeNet实现了更准确且连续的气道结构预测。特别是，TfeNet在目前最大的气道分割数据集Airway Tree Modeling（ATM22）上取得了94.95%的最高总分，并在肺纤维化数据集（AIIB23）上展示了先进性能。代码可在https://github.com/QibiaoWu/TfeNet获取。
英文摘要：
Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is therefore a prerequisite for enabling rapid bronchoscopic navigation and the clinical deployment of bronchoscopic robotic systems. Although convolutional neural network methods have gained considerable attention in airway segmentation, the unique tree-like structure of airways poses challenges for conventional and deformable convolutions, which often fail to focus on fine airway structures, leading to missed segments and discontinuities. To address this issue, this study proposes a novel tubular feature extraction network, named TfeNet. TfeNet introduces a novel direction-aware convolution operation that first applies spatial rotation transformations to adjust the sampling positions of linear convolution kernels. The deformed kernels are then represented as line segments or polylines in 3D space. Furthermore, a tubular feature fusion module (TFFM) is designed based on asymmetric convolution and residual connection strategies, enhancing the network's focus on subtle airway structures. Extensive experiments conducted on one public dataset and two datasets used in airway segmentation challenges demonstrate that the proposed TfeNet achieves more accuracy and continuous airway structure predictions compared with existing methods. In particular, TfeNet achieves the highest overall score of 94.95% on the current largest airway segmentation dataset, Airway Tree Modeling(ATM22), and demonstrates advanced performance on the lung fibrosis dataset(AIIB23). The code is available at https://github.com/QibiaoWu/TfeNet.
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；School of Computer Science and Technology, Guizhou University（贵州大学计算机科学与技术学院）；Shanghai Chest Hospital, Shanghai Jiao Tong University（上海交通大学附属胸科医院）；Université de Lyon, INSA Lyon, CREATIS, CNRS, INSERM（里昂大学、里昂国立应用科学学院、CREATIS实验室、法国国家科学研究中心、法国国家健康与医学研究院）
Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly i…
Accurate, automatic and complete extraction of pulmonary airway in medical images plays an important role in analyzing thoracic CT volumes such as lung cancer detection, chronic obstructive pulmonary disease (COPD), and bronchoscopic-assisted surgery navigation. However, this task remains challenges, due to the complex tree-like structure of the airways. In this technical report, we use two-stage fully convolutional networks (FCNs) to automatically segment pulmonary airway in thoracic CT scans from multi-sites. Specifically, we firstly adopt a 3D FCN with U-shape network architecture to segmen…
Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）
Airway segmentation on CT scans is critical for pulmonary disease diagnosis and endobronchial navigation. Manual extraction of airway requires strenuous efforts due to the complicated structure and various appearance of airway. For automatic airway extraction, convolutional neural networks (CNNs) based methods have recently become the state-of-the-art approach. However, there still remains a challenge for CNNs to perceive the tree-like pattern and comprehend the connectivity of airway. To address this, we propose a voxel-connectivity aware approach named AirwayNet for accurate airway segmentat…
Northeastern University（东北大学）；Shenyang University of Technology（沈阳工业大学）；The Chinese University of Hong Kong（香港中文大学）；Second Affiliated Hospital of Dalian Medical University（大连医科大学附属第二医院）；Central Hospital Affiliated to Shenyang Medical College（沈阳医学院附属中心医院）
Accurate airway extraction from computed tomography (CT) images is a critical step for planning navigation bronchoscopy and quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). The existing methods are challenging to sufficiently segment the airway, especially the high-generation airway, with the constraint of the limited label and cannot meet the clinical use in COPD. We propose a novel two-stage 3D contextual transformer-based U-Net for airway segmentation using CT images. The method consists of two stages, performing initial and refined airway segmentation…
HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Hupan Lab（湖畔实验室）；Department of Biomedical Engineering, Fudan University（复旦大学生物医学工程系）；Department of Electrical and Computer Engineering, Johns Hopkins University（约翰斯·霍普金斯大学电气与计算机工程系）；The First Affiliated Hospital of College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas
Vision Lab and Dept. of Electrical and Computer Engineering, University of Dayton（视觉实验室和电气与计算机工程系，代顿大学）；Dept. of Computer Science, University of Georgia（计算机科学系，佐治亚大学）；The University of Toledo Medical Center（托莱多大学医学中心）；Honeywell International Inc.（霍尼韦尔国际公司）；South Bend Medical Foundation（南本迪医疗基金会）
Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Microelectronics, Shanghai University（上海大学微电子学院）；Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）
National Heart and Lung Institute, Faculty of Medicine, Imperial College London（帝国理工学院医学院国家心肺研究所）；Department of Bioengineering, Faculty of Engineering, Imperial College London（帝国理工学院工程系生物工程系）；Royal Brompton Hospital（皇家布朗普顿医院）
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure …
MasHeNe: A Benchmark for Head and Neck CT Mass Segmentation using Window-Enhanced Mamba with Frequency-Domain Integration
University of Science, VNU-HCM, Ho Chi Minh City, Vietnam（越南胡志明市VNU-HCM大学科学学院）；Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学）；Thong Nhat Hospital, Ho Chi Minh City, Vietnam（胡志明市通那特医院）；University of Social Sciences and Humanities, VNU-HCM, Ho Chi Minh City, Vietnam（越南胡志明市VNU-HCM大学社会科学与人文学院）；Dublin City University, Dublin, Ireland（都柏林城市大学）；University of Health Sciences, VNU-HCM, Ho Chi Minh City, Vietnam（越南胡志明市VNU-HCM大学健康科学学院）；John von Neumann Institute, VNU-HCM, Ho Chi Minh City, Vietnam（约翰·冯·诺依曼研究所）
Peking University（北京大学）；The Hong Kong University of Science and Technology（香港科技大学）；Deepwise AI Lab（深智城人工智能实验室）
Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods based on convolutional neural networks (CNNs) may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. However, 3D convolutions are computationally inefficient, which prohibits the 3D CNNs from sufficiently large receptive fields to capture the global cues in the entire image. In this work, we propose a hybrid re…
Image Sciences Institute（影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Department of Radiology（放射科）
Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices in the mesh graph. The method was trained and evaluated using the publicly available Coronary Artery Stenoses Detection and Quantification Evaluation …
A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation
School of Mechanical Engineering, Hubei University of Technology（湖北工业大学机械工程学院）；National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University（南京大学新型软件技术国家重点实验室，计算机科学与技术系）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong（香港中文大学顺安先进工程研究院）
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Shenzhen University General Hospital（深圳大学总医院）；College of Mechatronics and Engineering, Shenzhen University（深圳大学机电与工程学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Research Center of Medical Plasma Technology, Shenzhen University（深圳大学医用等离子体技术研究中心）；Shenzhen Lihuy Medical Tech. co. Ltd.（深圳利护医疗科技有限公司）
