Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentation
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Shanghai Chest Hospital（上海市胸科医院）；Shanghai Engineering Research Center of Respiratory Endoscopy（上海市呼吸内镜工程技术研究中心）
Hao Zheng, Yulei Qin, Yun Gu, Fangfang Xie, Jie Yang, Jiayuan Sun, Guang-zhong Yang
Automated airway segmentation is a prerequisite for pre-operative diagnosis and intra-operative navigation for pulmonary intervention. Due to the small size and scattered spatial distribution of peripheral bronchi, this is hampered by severe class imbalance between foreground and background regions, which makes it challenging for CNN-based methods to parse distal small airways. In this paper, we demonstrate that this problem is arisen by gradient erosion and dilation of the neighborhood voxels. During back-propagation, if the ratio of the foreground gradient to background gradient is small while the class imbalance is local, the foreground gradients can be eroded by their neighborhoods. This process cumulatively increases the noise information included in the gradient flow from top layers to the bottom ones, limiting the learning of small structures in CNNs. To alleviate this problem, we use group supervision and the corresponding WingsNet to provide complementary gradient flows to enhance the training of shallow layers. To further address the intra-class imbalance between large and small airways, we design a General Union loss function which obviates the impact of airway size by distance-based weights and adaptively tunes the gradient ratio based on the learning process. Extensive experiments on public datasets demonstrate that the proposed method can predict the airway structures with higher accuracy and better morphological completeness than the baselines.
Shanghai Jiao Tong University（上海交通大学）；INSA Lyon（里昂国立应用科学学院）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；Université de Lyon（里昂大学）；Guizhou University（贵州大学）；Shanghai Chest Hospital（上海市胸科医院）
To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings <3 and >3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmo…
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；University of Parma（帕尔马大学）；Shanghai MicroPort MedBot (Group) Co., Ltd.（上海微创医疗机器人（集团）股份有限公司）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Cambridge International Exam Centre in Shanghai Experimental School（上海实验学校剑桥国际考试中心）；InferVision Medical Technology Co., Ltd.（推想医疗科技股份有限公司）；Shanghai MediWorks Precision Instruments Co., Ltd（上海美沃克斯精密仪器有限公司）；Sanmed Biotech Ltd.（三曼生物科技有限公司）；University of Cambridge（剑桥大学）；University of Palermo（巴勒莫大学）；IRCCS Istituto delle Scienze Neurologiche di Bologna（IRCCS博洛尼亚神经科学研究所）；Istituto Italiano di Tecnologia（意大利技术研究院）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；INSA Rouen Normandie（鲁昂国立应用科学学院）；Univ Rouen Normandie（鲁昂诺曼底大学）；Université Le Havre Normandie（勒阿弗尔诺曼底大学）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）；Wake Forest University School of Medicine（维克森林大学医学院）；University College London（伦敦大学学院）；King's College London（伦敦国王学院）
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…
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
Shanghai Jiao Tong University（上海交通大学）；InferVision Medical Technology Co., Ltd.（推想医疗科技股份有限公司）；University of California, Los Angeles（加州大学洛杉矶分校）；Imperial College London（帝国理工学院）
AirMorph: Topology-Preserving Deep Learning for Pulmonary Airway Analysis
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Shanghai Chest Hospital（上海市胸科医院）
University College London（伦敦大学学院）；KU Leuven（鲁汶大学）；University Hospitals Leuven（鲁汶大学医院）；University of Antwerp（安特卫普大学）
Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are characterised by abnormal dilatation of the airways. Quantification of airway features on computed tomography (CT) can help characterise disease progression. Physics based airway measurement algorithms have been developed, but have met with limited success in part due to the sheer diversity of airway morphology seen in clinical practice. Supervised learning methods are also not feasible due to the high cost of obtaining precise airway annotations. We propose synthesising airways by style transfer using perceptual losse…
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…
Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide. Yet, COPD diagnosis heavily relies on spirometric examination as well as functional airway limitation, which may cause a considerable portion of COPD patients underdiagnosed especially at the early stage. Recent advance in deep learning (DL) has shown their promising potential in COPD identification from CT images. However, with heterogeneous syndromes and distinct phenotypes, DL models trained with CTs from one data center fail to generalize on images from another center. Due to privacy regularization…
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…
Sichuan University - Pittsburgh Institute（四川大学-匹兹堡学院）
Airway segmentation from chest computed tomography scans has played an essential role in the pulmonary disease diagnosis. The computer-assisted airway segmentation based on the U-net architecture is more efficient and accurate compared to the manual segmentation. In this paper we employ the U$^2$-net trained by the Dice loss function to model the airway tree from the multi-site CT scans based on 299 training CT scans provided by the ATM'22. The derived saliency probability map from the training is applied to the validation data to extract the corresponding airway trees. The observation shows t…
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
Accurately and precisely characterizing the morphology of small pulmonary structures from Computed Tomography (CT) images, such as airways and vessels, is becoming of great importance for diagnosis of pulmonary diseases. The smaller conducting airways are the major site of increased airflow resistance in chronic obstructive pulmonary disease (COPD), while accurately sizing vessels can help identify arterial and venous changes in lung regions that may determine future disorders. However, traditional methods are often limited due to image resolution and artifacts. We propose a Convolutional Neur…
Airway segmentation in computed tomography images can be used to analyze pulmonary diseases, however, manual segmentation is labor intensive and relies on expert knowledge. This manuscript details our contribution to MICCAI's 2022 Airway Tree Modelling challenge, a competition of fully automated methods for airway segmentation. We employed a previously developed deep learning architecture based on a modified EfficientDet (MEDSeg), training from scratch for binary airway segmentation using the provided annotations. Our method achieved 90.72 Dice in internal validation, 95.52 Dice on external va…
MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography
School of Electrical and Computer Engineering, Universidade Estadual de Campinas（坎皮纳斯州立大学电气与计算机工程学院）；University of Iowa（爱荷华大学）
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；King's College London（伦敦国王学院）；TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟（BRTA））；University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究所）；Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）
Purpose: Bronchoscopic intervention is a widely-used clinical technique for pulmonary diseases, which requires an accurate and topological complete airway map for its localization and guidance. The airway map could be extracted from chest computed tomography (CT) scans automatically by airway segmentation methods. Due to the complex tree-like structure of the airway, preserving its topology completeness while maintaining the segmentation accuracy is a challenging task. Methods: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from patholog…
Erasmus MC（伊拉斯姆斯医学中心）；Erasmus MC-Sophia Children Hospital（伊拉斯姆斯医学中心索菲亚儿童医院）；University of Copenhagen（哥本哈根大学）；Copenhagen University Hospital（哥本哈根大学医院）；Herlev-Gentofte Hospital（海莱乌-根措夫特医院）
This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: i) a dataset of pediatric patients including subjects with cystic fibrosis, ii) a subset of…
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…
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the…
School of Mechanical engineering and Automation, Fuzhou University（福州大学机械工程及自动化学院）；School of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Thoracic Department, Fujian Medical University Union Hospital（福建医科大学附属协和医院胸外科）
Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree. Our framework segments the overall airway and small branches via the multi-information fusion convol…
Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization
Yau Mathematical Sciences Center Tsinghua University（姚数学科学中心清华大学）；The First Affiliated Hospital of Guangzhou Medical University（广州医科大学第一附属医院）；China State Key Laboratory of Respiratory Disease（中国呼吸疾病国家重点实验室）；Academy of Mathematics and Systems Science Chinese Academy of Sciences（数学科学学院中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Thoracic Surgery and Oncology The First Affiliated Hospital of Guangzhou Medical University（胸外科与肿瘤科广州医科大学第一附属医院）
Pulmonary lobe segmentation is an important preprocessing task for the analysis of lung diseases. Traditional methods relying on fissure detection or other anatomical features, such as the distribution of pulmonary vessels and airways, could provide reasonably accurate lobe segmentations. Deep learning based methods can outperform these traditional approaches, but require large datasets. Deep multi-task learning is expected to utilize labels of multiple different structures. However, commonly such labels are distributed over multiple datasets. In this paper, we proposed a multi-task semi-super…
Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院放射科）；Canon Medical Systems (China)（佳能医疗系统（中国））
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；TECNALIA, Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA）泰克纳利亚研究中心）；Technical University of Munich（慕尼黑工业大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Alberta（阿尔伯塔大学）；The Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branch…
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Department of Respiratory and Critical Care Medicine, Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Engineering Research Center of Respiratory Endoscopy（上海市胸科医院呼吸与危重症医学科、呼吸内镜中心、上海市呼吸内镜工程技术研究中心）
Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary disease analysis. In recent years, convolutional neural networks (CNNs) have been widely used to delineate the bronchial tree. However, the segmentation results of the CNN-based methods usually include many discontinuous branches, which need manual repair in clinical use. A major reason for the breakages is that the appearance of the airway wall can be affected by the lung disease as well as the adjacency of the vessels, while the network tends to overfit to these special patterns in the training set. To lear…
