lung 分割论文

《4D Lung Tumor Segmentation via Shape Prior and Motion Cues 》

Abstract— Lung tumor segmentation is important for therapy in the radiation treatment of patients with thoracic malignancies. In this paper, we describe a 4D image segmentation method based on graph-cuts optimization, shape prior and optical flow. Due to small size, the location, and low contrast between the tumor and the surrounding tissue, tumor segmentation in 3D+t
is challenging. We performed 4D lung tumor segmentation in 5 patients, and in each case compared the results with the expertdelineated lung nodules. In each case, 4D image segmentation took approximately ten minutes on a PC with AMD Phenom II and 32GB of memory for segmenting tumor in five phases of lung CT data.

描述了基于图形切割优化，形状先验和光学流动的4D图像分割方法。

《3-D Lung Segmentation by Incremental Constrained Nonnegative Matrix Factorization》

Accurate lung segmentation from large-size 3-D chest-computed tomography images is crucial for computerassisted cancer diagnostics. To efficiently segment a 3-D lung,we extract voxel-wise features of spatial image contexts by unsupervised learning with a proposed incremental constrained nonnegative matrix factorization (ICNMF). The method applies smoothness constraints to learn the features, which are more robust to lung tissue inhomogeneities, and thus, help to better segment internal lung pathologies than the known state-of-the-art techniques.Compared to the latter, the ICNMF depends less on the domain expert knowledge and is more easily tuned due to only a few control parameters. Also, the proposed slice-wise incremental learning with due regard for interslice signal dependencies decreases the computational complexity of the NMF-based segmentation and is scalable to very large 3-D lung images. The method is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (seven datasets), in vivo datasets for 17 subjects,and 55 datasets from the Lobe and Lung Analysis 2011 (LOLA11)study. For the in vivo data, the accuracy of our segmentation w.r.t.the ground truth is 0.96 by the Dice similarity coefficient, 9.0 mm
by the modified Hausdorff distance, and 0.87% by the absolute lung volume difference, which is significantly better than for the NMF-based segmentation. In spite of not being designed for lungs with severe pathologies and of no agreement between radiologists on the ground truth in such cases, the ICNMF with its total accuracy of 0.965 was ranked fifth among all others in the LOLA11.After excluding the nine too pathological cases from the LOLA11 dataset, the ICNMF accuracy increased to 0.986.

通过提出的增量约束非负矩阵因式分解（ICNMF）通过无监督学习提取空间图像上下文的体素特征。

《Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling
》

To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternatesubordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4±1.0%, 2.2±1.0 mm, 0.42±0.10%) and the VESSEL12 database (99.0±0.5%, 2.1±1.6 mm, 0.39±0.20%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of  the LOLA11 competition, where an average overlap of 98.0% with the expert’s segmentation is yielded on all 55 subjects with our framework being ranked first among all the stateof-the-art techniques compared.

为了准确分割病理和健康的肺，用于可靠的计算机辅助疾病诊断，将一叠胸部CT扫描建模为空间不均匀联合3D马科夫吉布斯随机场（MGRF）的体素肺和胸部CT图像信号的样本（强度）。拟议的可学习的MGRF集成了两个具有适应性肺形状子模型的视觉外观子模型。一阶外观子模型分别计算原始CT图像及其高斯尺度空间（GSS）滤波版本，以分别指定局部和全局信号属性。信号的每个经验边际概率分布与离散高斯（LCDG）的线性组合密切相似，其包含两个正优势和多个符号交替的辅助DG。将近似值分成两个LCDG，分别描述肺及其背景，即所有其他胸部组织。二级外观子模型量化原始GSS滤波图像中最近的体素26邻域中的条件成对强度依赖性。形状子模型是针对一组训练数据构建的，并且在使用肺和胸部外观的分割期间进行修改。
《Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
》

Pathological lung segmentation (PLS) is an important, yet challenging, medical image application due to the wide variability of pathological lung appearance and shape. Because PLS is often a prerequisite for other imaging analytics, methodological simplicity and generality are key factors in usability. Along those lines, we present a bottomup deep-learning based approach that is expressive enough to handle variations in appearance, while remaining unaffected by any variations in shape. We incorporate the deeply supervised learning framework, but enhance it with a simple, yet effective, progressive multi-path scheme,which more reliably merges outputs from different network stages. The result is a deep model able to produce finer detailed masks, which we call progressive holistically-nested networks (P-HNNs). Using extensive cross-validation, our method is tested on multi-institutional datasets comprising 929 CT scans (848 publicly available), of pathological lungs, reporting mean dice scores of 0:985 and demonstrating significant qualitative and quantitative improvements over state-of-the art approaches.

《Automatic Lung Segmentation Using Control Feedback System:Morphology and Texture Paradigm
》

Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA).The left lung’s performance of segmentation was 96.52 % for Jaccard Index and 98.21 % for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), −1.15 % for Relative Area Error and 4.09 % Area Overlap Error. The right lung’s performance of segmentation was 97.24 % for Jaccard Index,98.58 % for Dice Similarity, 0.61 mm for PDM, −0.03 % for Relative Area Error and 3.53 % for Area Overlap Error. The segmentation overall has an overall similarity of 98.4 %. The segmentation proposed is an accurate and fully automated system.

提出的自动分割使用初始阈值和基于形态的分割与反馈进行匹配，该反馈通过校正分割检测大偏差。该反馈类似于控制系统，其允许检测异常或严重的肺部疾病，并且提供对在线分割的反馈，从而提高系统的整体性能。

《Joint Lung CT Image Segmentation:A Hierarchical Bayesian Approach
》

Accurate lung CT image segmentation is of great clinical value, especially when it comes to delineate pathological regions including lung tumor. In this paper, we present a novel framework that jointly segments multiple lung computed tomography (CT) images via hierarchical Dirichlet process (HDP). In specifics, based on the assumption that lung CT images from different patients share similar image structure (organ sets and relative positioning), we derive a mathematical model to segment them simultaneously so that shared information across patients could be utilized to regularize each individual segmentation. Moreover, compared to many conventional models, the algorithm requires little manual involvement due to the nonparametric nature of Dirichlet process (DP). We validated proposed model upon clinical data consisting of healthy and abnormal (lung cancer) patients. We demonstrate that, because of the joint segmentation fashion, more accurate and consistent segmentations could be obtained.

提出了一个新颖的框架，通过分层Dirichlet过程（HDP）联合分割多个肺计算机断层扫描（CT）图像。

《AUTOMATIC SEGMENTATION OF PATHOLOGICAL LUNG USING INCREMENTAL NONNEGATIVE MATRIX FACTORIZATION
》

Accurate segmentation of pathological lungs from large-size chest computed tomographic images is crucial for computer-assisted lung cancer diagnostics. In this paper, a new framework for automatic pathological lung segmentation is proposed. The proposed INMF-based segmentation approach has the ability to handle the in-homogeneities caused by the arteries, veins, bronchi, and possible pathologies that may exist in the lung tissues, and to detect the number of clusters in the image in an automated manner. The proposed INMF-based segmentation framework is quantitatively validated on simulated realistic lung phantoms that mimic different lung pathologies (7 datasets), in vivo data sets for 17 subjects, and for lung disease with severe pathologies. Three metrics are used: the Dice coefficient, modified Hausdorff distance, and absolute lung volume difference. Results show that the proposed approach outperforms existing lung segmentation techniques and can handle in-homogenities caused by different pathologies.

《Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images
》

The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level-set algorithm based on coherent propagation and the semi-automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F-measure of 99.22%, revealing its superiority and competency to segment lungs in CT images.

这项工作提出了一种新颖强大的技术，称为3D自适应脆性主动轮廓法（3D ACACM），用于CT肺图像的分割。该方法开始于待分割的肺内的球体，其被作用在其上的力朝向肺边界变形。