lung 分割論文

《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.

描述了基於圖形切割優化，形狀先驗和光學流動的4D圖像分割方法。

《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.

經過提出的增量約束非負矩陣因式分解（ICNMF）經過無監督學習提取空間圖像上下文的體素特徵。

《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.

爲了準確分割病理和健康的肺，用於可靠的計算機輔助疾病診斷，將一疊胸部CT掃描建模爲空間不均勻聯合3D馬科夫吉布斯隨機場（MGRF）的體素肺和胸部CT圖像信號的樣本（強度）。擬議的可學習的MGRF集成了兩個具備適應性肺形狀子模型的視覺外觀子模型。一階外觀子模型分別計算原始CT圖像及其高斯尺度空間（GSS）濾波版本，以分別指定局部和全局信號屬性。信號的每一個經驗邊際機率分佈與離散高斯（LCDG）的線性組合密切類似，其包含兩個正優點和多個符號交替的輔助DG。將近似值分紅兩個LCDG，分別描述肺及其背景，即全部其餘胸部組織。二級外觀子模型量化原始GSS濾波圖像中最近的體素26鄰域中的條件成對強度依賴性。形狀子模型是針對一組訓練數據構建的，而且在使用肺和胸部外觀的分割期間進行修改。
《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.

提出的自動分割使用初始閾值和基於形態的分割與反饋進行匹配，該反饋經過校訂分割檢測大誤差。該反饋相似於控制系統，其容許檢測異常或嚴重的肺部疾病，而且提供對在線分割的反饋，從而提升系統的總體性能。

《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.

提出了一個新穎的框架，經過分層Dirichlet過程（HDP）聯合分割多個肺計算機斷層掃描（CT）圖像。

《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.

這項工做提出了一種新穎強大的技術，稱爲3D自適應脆性主動輪廓法（3D ACACM），用於CT肺圖像的分割。該方法開始於待分割的肺內的球體，其被做用在其上的力朝向肺邊界變形。