Reliable and automatic segmentation of lung lobes is important for diagnosis, assessment, and quantification of pulmonary diseases. The existing techniques are prohibitively slow, undesirably rely on prior (airway/vessel) segmentation, and/or require user interactions for optimal results. This work presents a reliable, fast, and fully automated lung lobe segmentation based on a progressive dense V-network (PDV-Net). The proposed method can segment lung lobes in one forward pass of the network, with an average runtime of 2 seconds using 1 Nvidia Titan XP GPU, eliminating the need for any prior atlases, lung segmentation or any subsequent user intervention. We evaluated our model using 84 chest CT scans from the LIDC and 154 pathological cases from the LTRC datasets. Our model achieved a Dice score of $0.939 \pm 0.02$ for the LIDC test set and $0.950 \pm 0.01$ for the LTRC test set, significantly outperforming a 2D U-net model and a 3D dense V-net. We further evaluated our model against 55 cases from the LOLA11 challenge, obtaining an average Dice score of 0.935---a performance level competitive to the best performing team with an average score of 0.938. Our extensive robustness analyses also demonstrate that our model can reliably segment both healthy and pathological lung lobes in CT scans from different vendors, and that our model is robust against configurations of CT scan reconstruction.
Peking University（北京大学）；Beijing Institute of Big Data Research（北京大数据研究院）；Alibaba Group（阿里巴巴集团）
The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we t…
Automatic pathological pulmonary lobe segmentation(PPLS) enables regional analyses of lung disease, a clinically important capability. Due to often incomplete lobe boundaries, PPLS is difficult even for experts, and most prior art requires inference from contextual information. To address this, we propose a novel PPLS method that couples deep learning with the random walker (RW) algorithm. We first employ the recent progressive holistically-nested network (P-HNN) model to identify potential lobar boundaries, then generate final segmentations using a RW that is seeded and weighted by the P-HNN …
Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compared the results to two other methods. The new loss function improved the mean distance to 1.46 mm (compared to 2.08 mm for simple loss function without weighting).
Detection of pulmonary nodules on chest CT is an essential step in the early diagnosis of lung cancer, which is critical for best patient care. Although a number of computer-aided nodule detection methods have been published in the literature, these methods still have two major drawbacks: missing out true nodules during the detection of nodule candidates and less-accurate identification of nodules from non-nodule. In this paper, we propose an automated pulmonary nodule detection algorithm that jointly combines progressive resolution and hierarchical saliency. Specifically, we design a 3D progr…
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…
University of Oxford（牛津大学）；Universiti Malaysia Perlis（马来西亚玻璃市大学）；Churchill Hospital（丘吉尔医院）
The human lung is a complex respiratory organ, consisting of five distinct anatomic compartments called lobes. Accurate and automatic segmentation of these pulmonary lobes from computed tomography (CT) images is of clinical importance for lung disease assessment and treatment planning. However, this task is challenging due to ambiguous lobar boundaries, anatomical variations and pathological deformations. In this paper, we propose a high-resolution and efficient 3D fully convolutional network to automatically segment the lobes. We refer to the network as Pulmonary Lobe Segmentation Network (PL…
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…
Automatic lobe segmentation using attentive cross entropy and end-to-end fissure generation
Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）；Shanghai AI Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；West China Biomedical Big Data Center, Sichuan University West China Hospital（四川大学华西医院华西生物医学大数据中心）
University of California, Irvine（加州大学欧文分校）；Deep Voxel Inc.（深体素公司）
Pulmonary lobe segmentation is an important task for pulmonary disease related Computer Aided Diagnosis systems (CADs). Classical methods for lobe segmentation rely on successful detection of fissures and other anatomical information such as the location of blood vessels and airways. With the success of deep learning in recent years, Deep Convolutional Neural Network (DCNN) has been widely applied to analyze medical images like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which, however, requires a large number of ground truth annotations. In this work, we release our manuall…
A Patient-Specific Pulmonary Arterial Tree Digital Twin to Extract Pulmonary Embolism Biomarkers
Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294（里昂大学、里昂国立应用科学学院、 Claude Bernard 里昂大学、 UJM-圣艾蒂安、 CNRS、 Inserm、 CREATIS UMR 5220、 U1294）；Department of Radiology, CHU Saint-Etienne, UJM Saint-Etienne, Saint-Etienne, France（放射科、圣艾蒂安大学医院、 UJM-圣艾蒂安、圣艾蒂安、法国）；IUF, Institut Universitaire de France, Paris（IUF、法国国家科学院、巴黎）
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））
Imperial College London（伦敦帝国理工学院）；Imperial College NHS Trust（帝国理工学院国民医疗服务信托）
Automated lobar segmentation allows regional evaluation of lung disease and is important for diagnosis and therapy planning. Advanced statistical workflows permitting such evaluation is a needed area within respiratory medicine; their adoption remains slow, with poor workflow accuracy. Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes due to oblique or lacking fissures. This impact motivated developing an improved machine learning method to segment lung lobes that utilises tracheobronchial tree information to enhance…
M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification
Hefei Cancer Hospital of CAS, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥医疗健康研究院、健康与医疗技术研究所、物理研究所）；University of Science and Technology of China（中国科学技术大学）；Graduate School, Bengbu Medical College（蚌埠医疗学院研究生院）；Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)（中国科学技术大学附属第一医院呼吸与危重症医学科、生命科学与医学学院）；Northeastern University（东北大学）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；College of Computing and Data Science (CCDS), Nanyang Technological University（南洋理工大学计算与数据科学学院）
Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic lung disease with a variable and unpredictable rate of progression. CT scans of the lungs inform clinical assessment of IPF patients and contain pertinent information related to disease progression. In this work, we propose a multi-modal method that uses neural networks and memory banks to predict the survival of IPF patients using clinical and imaging data. The majority of clinical IPF patient records have missing data (e.g. missing lung function tests). To this end, we propose a probabilistic model that captures the de…
University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（DarwinAI公司）
Pulmonary fibrosis is a devastating chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and has no known cure. A critical step in the treatment and management of pulmonary fibrosis is the assessment of lung function decline, with computed tomography (CT) imaging being a particularly effective method for determining the extent of lung damage caused by pulmonary fibrosis. Motivated by this, we introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression fro…
Medical image computing has advanced rapidly with the advent of deep learning techniques such as convolutional neural networks. Deep convolutional neural networks can perform exceedingly well given full supervision. However, the success of such fully-supervised models for various image analysis tasks (e.g., anatomy or lesion segmentation from medical images) is limited to the availability of massive amounts of labeled data. Given small sample sizes, such models are prohibitively data biased with large domain shift. To tackle this problem, we propose a novel end-to-end medical image segmentatio…
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis
Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；Department of Med. Physics & Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系）；Department of Respiratory Medicine, University Hospitals Leuven（鲁汶大学医院呼吸医学科）；NIHR Southampton Biomedical Research Centre and Clinical and Experimental Sciences, University of Southampton（南安普顿大学NIHR南安普顿生物医学研究中心与临床与实验科学系）；UCL Respiratory, University College London（伦敦大学学院呼吸科）
Human and AI collaboration for pulmonary nodule segmentation
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学科学国家重点实验室，数学与系统科学研究院，中国科学院）；PET/CT Center, The First Affiliated Hospital of Guangzhou Medical University（广州医科大学第一附属医院PET/CT中心）；Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University（广州医科大学第一附属医院胸外科与肿瘤科）；Department of Mathematical Science, Tsinghua University（清华大学数学科学系）；Yau Mathematical Sciences Center, Tsinghua University（清华大学尤金数学科学中心）；China State Key Laboratory of Respiratory Disease & National Clinical Research Centre for Respiratory Disease, Guangzhou, China（中国呼吸疾病国家重点实验室及呼吸疾病临床研究中心，广州，中国）；School of Mathematical Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）；National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University（呼吸医学国家中心、呼吸疾病临床研究中心、广州呼吸健康研究院、广州医科大学第一附属医院）
King’s College London（伦敦国王学院）；National Hospital for Neurology and Neurosurgery（国家神经内科和神经外科医院）；Thomas Jefferson University（托马斯·杰斐逊大学）；University of Washington（华盛顿大学）
Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Region of interest (ROI) segmentation before and after LITT would enable automated lesion quantification to objectively assess treatment efficacy. Deep learning techniques, such as convolutional neural networks (CNNs) are state-of-the-art solutions for ROI segmentation, but require large amounts of annotated data during the training. However, collecting large datasets from emerging treatments such as LITT is impractical.…
Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
Association of Progressive PPFE and Mortality in Lung Cancer Screening Cohorts
Satsuma Lab, Hawkes Institute, University College London, London, UK；Department of Respiratory Medicine, University College London, London, UK；Department of Computer Science, University College London, London, UK；Guy’s Cancer Centre, Guy’s；Radiology Department, Hospital de la Santa Creu i Sant Pau, Sant Quintí, Barcelona, Spain；Department of Medicine, University of Iceland, Iceland；NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Central Manchester University Hospitals, NHS Foundation Trust, Manchester, UK；Data Sciences, School of Health Sciences, University of Manchester, Manchester, UK；UCL Hawkes Institute, University College London, London, UK；Lungs for Living, Department of Respiratory Medicine, University College London, London, UK
