浏览全部资源
扫码关注微信
收稿：2018-02-27，
修回：2018-5-3，
纸质出版：2018-10-16
移动端阅览
目的
2
高分辨率多层螺旋CT是临床医生研究肺部解剖结构功能、评估生理状态、检测和诊断病变的主要影像学工具。鉴于肺部各解剖结构间特殊的关联关系和图像成像缺陷、组织病变等干扰因素对分割效果的影响，学术界已在经典图像处理方法基础上针对CT图像中的肺部解剖结构分割进行了大量研究。
方法
2
通过对相关领域有代表性或前沿性文献的归纳总结，系统性地梳理了现有肺组织、肺气管、肺血管、肺裂纹、肺叶或肺段等解剖结构CT图像分割方法的主要流程、方法理论、关键技术和优缺点，讨论了各解剖结构分割的参考数据获取、实验设计方法和结果评价指标。
结果
2
分析了现有研究在结果精度和鲁棒性方面所面临的挑战性问题，以及基于分割结果在定位病变、定量测量、提取其他结构等方面展开的热点应用，特别详述了当前被重点关注的深度学习方法在本领域的工作进展，同时展望了本领域在分割理论方法和后续处理等步骤的发展趋势，并探索了如何在实践中根据分割结果发现新的临床生物标志。
结论
2
快速精确地从CT图像中分割肺部各解剖结构可以获取清晰直观的3维可视化结构影像，展开解剖结构内部的定量参数测量或结构之间的关联关系分析能提供客观、有效的肺部组织疾病辅助诊断依据信息，可以大大减轻临床医生的阅片负担、提高工作效率，具有重要的理论研究意义和临床应用价值。
Objective
2
Pulmonary disorder has high morbidity and mortality world-wide according to the reports by the World Health Organization.Some common pulmonary disorders include lung nodule and cancer
interstitial lung disease
chronic obstructive pulmonary disease
bronchiectasis
and pulmonary embolism.The disorders are typically characterized by long-term poor breath quality
irregular blood supply
and obstructive airflow and circulation.Pulmonary disorders bring not only enormous societal financial burden but also physical and mental suffering to patients.Thus
the recognition and comprehension of the disorders are widely considered the most basic and crucial medical tasks.High-resolution multi-slice computed tomography (CT) has received significant attention from pulmonologists and radiologists due to its allowance of investigating pulmonary anatomic function
assessing physiological conditions
and detecting and diagnosing pulmonary disorders.Hundreds of isotropic thin slices that are reconstructed real time from a single spiral CT scan can realize objective
repeatable
and non-invasive clinical inspections unlike traditional tools
especially in the early disease stage.However
the manual delineation
measurement
and evaluation of volumetric scans are extremely time-consuming and entail intensively laborious work load for clinicians.Therefore
the biomedical engineering community aims to develop semi-automated and fully automated segmentations in CT images through voxel-by-voxel labeling by using computer software to separate sub-divided pulmonary anatomic structures from one another.In the presence of unique inter-and intra-anatomy relationships and the impact of imaging defects
abnormalities
or other interference factors
classical image processing methods suffer from performance limitations.The anatomic CT visibility is attenuated and the morphology is deformed spatially and pathologically
which affect the segmentation results negatively.Several studies
usually incorporating traditional work and carefully defined processing rules
that focus on thoracic CT images have been employed.
Method
2
In this paper
a systematic review of anatomic segmentation methods for pulmonary tissues
airways
vasculatures
fissures
and lobes is presented by tracking and summarizing the representative or up-to-date published literature.In addition
several attractive practices and extractions of sub-divided or related structures
derived from segmented anatomic results
are also attached to corresponding anatomic subsections.They include the segmentation of adhesive
pleura nodular
and interstitial diseased lungs; the centerline extraction of airways and vasculatures; airway wall quantification and segmentation; pulmonary artery and vein separation; and pulmonary segment approximation.Moreover
for all the referred segmentation methods
the full implementation pipeline
background image processing methodology
and key techniques are presented to elaborate the result performance.Analogous methods are further classified on the basis of their designed frameworks or mathematical theories
and the merits and demerits of each method type are analyzed at the end of the classification content.In general
evaluating segmentations and comparing with other work are tedious for researchers mainly because of the difficulty in obtaining the ground truth.LOLA11
EXACT09
and VESSEL12 are three of the public and authoritative MICCAI grand challenges in chest image analysis.They are introduced for result comparisons in the directions of pulmonary tissue and lobe
airway
and vasculature.The complete procedure renders it difficult for the owners to construct their reference repository and quantify the submission performance.The reported standard generation approaches to other anatomic-based applications are illuminated in parallel.The evaluated indices contain the anatomic boundary alignment and volume overlap and the trade-off between true and false positive detection.Subsequently
the experimental validation approaches are explained based on the reference standard and indices.The qualitative and quantitative results of different methods are shown specifically with the description of the test datasets.
Result
2
On the basis of the individual anatomic topic
the existing challenges of state-of-the-art studies are presented in detail
highlighting the accuracy performance in true positive detection and false positive removal and the robustness performance against the diversity of CT scanners
imaging protocols
and the appearance of various abnormalities.A set of practical problems
such as lesion location
qualitative and quantitative anatomic measurement
and sequential component segmentation
are also discussed in the paper.Besides deep learning algorithms
convolutional neural network-based algorithms have rapidly become a preference of medical imaging institutions.At present
two mature chest CT imaging fields exist
namely
nodule detection and malignancy prediction in lung cancer screening and interstitial lung disease type classification.The major deep learning-based efforts are surveyed in terms of their contribution to pulmonary tissue bounding box localization and pulmonary airway segmentation and leakage removal
most of which were proposed in the recent two years.The improvements are compared with previous methods.In terms of the frontier requirements from scientific groups
industrial units
and pulmonology domains
future work trends and open issues are listed pertinent to methodology and post-processing steps along with the applications
such as the identification of pulmonary lesion sites and the subsequent task of anatomic segmentation.The parameters of anatomic measurement are of vital importance in characterizing the progress and severity of pulmonary disorders.Thus
several possible innovative points to achieve these biomarkers are also recommended.
Conclusion
2
Theoretical studies and clinical practices will benefit from implementing accurate
fast
and robust pulmonary anatomic segmentation from large amounts of CT images.The transfer or modification of successful pulmonary segmentation methodologies will facilitate the segmentation of other tissues
organs
and multi-modality images.Inter-and intra-structure measurements and the relationship mapping information obtained
ranging from global to local analyses
can provide objective and effective evidence for computer-aided pulmonary disease detection and diagnosis.The 2D transversal or 3D visualization of these points can present intuitive
legible
and proportional views of the anatomic structures with the help of volume rendering techniques and grayscale Dicom slices overlaid by chromatic tissue marks.The contribution of these aspects reduces the labor requirement from pulmonologists and radiologists
thereby increasing their efficiency significantly.Although deep learning algorithms are relatively immature and require improvement in terms of segmentation time cost and refinement steps
they have considerable potential and are worth studying for pulmonary segmentation areas
which are predicted to dominate the field in the coming years.
Burgel P R.The role of small airways in obstructive airway diseases[J].European Respiratory Review, 2011, 20(119):23-33.[DOI:10.1183/09059180.00010410]
Sluimer I, Schilham A, Prokop M, et al.Computer analysis of computed tomography scans of the lung:a survey[J].IEEE Transactions on Medical Imaging, 2006, 25(4):385-405.[DOI:10.1109/TMI.2005.862753]
van Ginneken B.Fifty years of computer analysis in chest imaging:rule-based, machine learning, deep learning[J].Radiological Physics and Technology, 2017, 10(1):23-32.[DOI:10.1007/s12194-017-0394-5]
van Rikxoort E M, van Ginneken B.Automated segmentation of pulmonary structures in thoracic computed tomography scans:a review[J].Physics in Medicine&Biology, 2013, 58(17):R187-R220.[DOI:10.1088/0031-9155/58/17/R187]
Pu J T, Gu S C, Liu S S, et al.CT based computerized identification and analysis of human airways:a review[J].Medical Physics, 2012, 39(5):2603-2616.[DOI:10.1118/1.4703901]
van Ginneken B, Armato Ⅲ S G, de Hoop B, et al.Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans:the ANODE09 study[J].Medical Image Analysis, 2010, 14(6):707-722.[DOI:10.1016/j.media.2010.05.005]
Armato Ⅲ S G, McLennan G, Bidaut L, et al.Data From LIDC-IDRI.The Cancer Imaging Archive (2015). http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX http://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
Setio A A A, Traverso A, de Bel T, et al.Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images:the LUNA16 chal-lenge[J].Medical Image Analysis, 2017, 42:1-13.[DOI:10.1016/j.media.2017.06.015]
Doel T, Gavaghan D J, Grau V.Review of automatic pulmonary lobe segmentation methods from CT[J].Computerized Medical Imaging and Graphics, 2015, 40:13-29.[DOI:10.1016/j.compmedimag.2014.10.008]
van Rikxoort E M, de Hoop B, van de Vorst S, et al.Automatic segmentation of pulmonary segments from volumetric chest CT scans[J].IEEE Transactions on Medical Imaging, 2009, 28(4):621-630.[DOI:10.1109/TMI.2008.2008968]
Lo P, van Ginneken B, Reinhardt J M, et al.Extraction of airways from CT (EXACT'09)[J].IEEE Transactions on Medical Imaging, 2012, 31(11):2093-2107.[DOI:10.1109/TMI.2012.2209674]
Rudyanto R D, Kerkstra S, van Rikxoort E M, et al.Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung:the VESSEL12 study[J].Medical Image Analysis, 2014, 18(7):1217-1232.[DOI:10.1016/j.media.2014.07.003]
Mansoor A, Bagci U, Foster B, et al.Segmentation and image analysis of abnormal lungs at CT:current approaches, challenges, and future trends[J].RadioGraphics, 2015, 35(4):1056-1076.[DOI:10.1148/rg.2015140232]
Hu S, Hoffman E A, Reinhardt J M.Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images[J].IEEE Transactions on Medical Imaging, 2001, 20(6):490-498.[DOI:10.1109/42.929615]
Sun X J, Zhang H B, Duan H C.3D computerized segmentation of lung volume with computed tomography[J].Academic Radiology, 2006, 13(6):670-677.[DOI:10.1016/j.acra.2006.02.039]
Zhang L, Reinhardt J M.3D pulmonary CT image registration with a standard lung atlas[C]//Proceedings of SPIE 3978, Medical Imaging 2000: Physiology and Function from Multidimensional Images.San Diego: SPIE, 2000: 67-77.[ DOI:10.1117/12.383441 http://dx.doi.org/10.1117/12.383441 ]
Li B J, Christensen G E, Hoffman E A, et al.Establishing a normative atlas of the human lung:intersubject warping and registration of volumetric CT images[J].Academic Radiology, 2003, 10(3):255-265.[DOI:10.1016/S1076-6332(03)80099-5]
Sluimer I, Prokop M, van Ginneken B.Toward automated segmentation of the pathological lung in CT[J].IEEE Transactions on Medical Imaging, 2005, 24(8):1025-1038.[DOI:10.1109/TMI.2005.851757]
Sun S H, Bauer C, Beichel R.Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach[J].IEEE Transactions on Medical Imaging, 2012, 31(2):449-460.[DOI:10.1109/TMI.2011.2171357]
Chae S H, Moon H M, Chung Y, et al.Automatic lung segmentation for large-scale medical image management[J].Multimedia Tools and Applications, 2016, 75(23):15347-15363.[DOI:10.1007/s11042-014-2201-1]
He N N, Zhang X L, Zhao J J, et al.Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity[C]//Proceedings of SPIE 10420, Ninth International Conference on Digital Image Processing.Hong Kong, China: SPIE, 2017: #104202G.[ DOI:10.1117/12.2281942 http://dx.doi.org/10.1117/12.2281942 ]
Zheng B, Leader J K, Maitz G S, et al.A simple method for automated lung segmentation in X-ray CT images[C]//Proceedings of SPIE 2032, Medical Imaging 2003: Image Processing.San Diego: SPIE, 2003: 1455-1463.[ DOI:10.1117/12.480290 http://dx.doi.org/10.1117/12.480290 ]
De Nunzio G, Tommasi E, Agrusti A, et al.Automatic lung segmentation in CT images with accurate handling of the hilar region[J].Journal of Digital Imaging, 2011, 24(1):11-27.[DOI:10.1007/s10278-009-9229-1]
Mansoor A, Bagci U, Xu Z Y, et al.A generic approach to pathological lung segmentation[J].IEEE Transactions on Medical Imaging, 2014, 33(12):2293-2310.[DOI:10.1109/TMI.2014.2337057]
van Ginneken B.LOLA11[DB/OL].2011-09-01[2018-02-03] . https://lola11.grand-challenge.org https://lola11.grand-challenge.org .
Wei Y, Shen G, Li J J.A fully automatic method for lung parenchyma segmentation and repairing[J].Journal of Digital Imaging, 2013, 26(3):483-495.[DOI:10.1007/s10278-012-9528-9]
Shen S W, Bui A A T, Cong J, et al.An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy[J].Computers in Biology and Medicine, 2015, 57:139-149.[DOI:10.1016/j.compbiomed.2014.12. 008]
Korfiatis P, Kalogeropoulou C, Karahaliou A, et al.Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT[J].Medical Physics, 2008, 35(12):5290-5302.[DOI:10.1118/1.3003066]
Wang J H, Li F, Li Q.Automated segmentation of lungs with severe interstitial lung disease in CT[J].Medical Physics, 2009, 36(10):4592-4599.[DOI:10.1118/1.3222872]
Noor N M, Than J C M, Rijal O M, et al.Automatic lung segmentation using control feedback system:morphology and texture paradigm[J].Journal of Medical Systems, 2015, 39(3):#22.[DOI:10.1007/s10916-015-0214-6]
Prasad M N, Brown M S, Ahmad S, et al.Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs[J].Academic Radiology, 2008, 15(9):1173-1180.[DOI:10.1016/j.acra.2008.02.004]
Pulagam A R, Kande G B, Ede V K R, et al.Automated lung segmentation from HRCT scans with diffuse parenchymal lung diseases[J].Journal of Digital Imaging, 2016, 29(4):507-519.[DOI:10.1007/s10278-016-9875-z]
Mamani G E H, Setio A A A, van Ginneken B, et al.Organ detection in thorax abdomen CT using multi-label convolutional neural networks[C]//Proceedings of SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis.Orlando: SPIE, 2017: #1013416.[ DOI:10.1117/12.2254349 http://dx.doi.org/10.1117/12.2254349 ]
Brown M S, McNitt-Gray M F, Goldin J G, et al.An extensible knowledge-based architecture for segmenting computed tomography images[C]//Proceedings of 1997 International Conference on Image Processing.Santa Barbara: IEEE, 1997: 516-519.[ DOI:10.1109/ICIP.1997.632171 http://dx.doi.org/10.1109/ICIP.1997.632171 ]
Armato Ⅲ S G, MacMahon H.Automated lung segmentation and computer-aided diagnosis for thoracic CT scans[J].International Congress Series, 2003, 1256:977-982.[DOI:10.1016/S0531-5131(03)00388-1]
Armato Ⅲ S G, Sensakovic W F.Automated lung segmentation for thoracic CT:impact on computer-aided diagnosis[J].Academic Radiology, 2004, 11(9):1011-1021.[DOI:10.1016/j.acra.2004.06.005]
Yim Y, Hong H.Smoothing segmented lung boundary in chest CT images using scan line search[C]//Proceedings of the 11th Iberoamerican Congress on Pattern Recognition.Cancun: Springer, 2006: 147-156.[ DOI:10.1007/11892755_15 http://dx.doi.org/10.1007/11892755_15 ]
Aykac D, Hoffman E A, McLennanG, et al.Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images[J].IEEE Transactions on Medical Imaging, 2003, 22(8):940-950.[DOI:10.1109/TMI.2003.815905]
Irving B, Taylor P, Todd-Pokropek A.3D segmentation of the airway tree using a morphology based method[C]//Proceedings of the 2nd International Workshop on Pulmonary Image Analysis.London: Create Space Independent Publishing Platform, 2009: 297-307.
Xu Z Y, Bagci U, Foster B, et al.A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT[J].Medical Image Analysis, 2015, 24(1):1-17.[DOI:10.1016/j.media.2015.05.003]
Feuerstein M, Kitasaka T, Mori K.Adaptive branch tracing and image sharpening for airway tree extraction in 3-D chest CT[C]//Proceedings of the 2nd International Workshop on Pulmonary Image Analysis.London: Create Space Independent Publishing Platform, 2009: 273-284.
Hirano Y, Xu R, Tachibana R, et al.A method for extracting airway tree by using a cavity enhancement filter[C]//Proceedings of the 4th International Workshop on Pulmonary Image Analysis.Toronto: Springer, 2011: 91-99.
Bauer C, Pock T, Bischof H, et al.Airway tree reconstruction based on tube detection[C]//Proceedings of the 2nd International Workshop on Pulmonary Image Analysis.London: Springer, 2009: 203-213.
Pu J T, Fuhrman C, Good W F, et al.A differential geometric approach to automated segmentation of human airway tree[J].IEEE Transactions on Medical Imaging, 2011, 30(2):266-278.[DOI:10.1109/TMI.2010.2076300]
Lo P, Sporring J, Ashraf H, et al.Vessel-guided airway tree segmentation:a voxel classification approach[J].Medical Image Analysis, 2010, 14(4):527-538.[DOI:10.1016/j.media.2010.03.004]
Meng Q E, Roth H R, Kitasaka T, et al.Tracking and segmentation of the airways in chest CT using a fully convolutional network[C]//Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention.Quebec City: Springer, 2017: 198-207.[DOI: DOI:10.1007/978-3-319-66185-8_23 http://dx.doi.org/10.1007/978-3-319-66185-8_23 ]
Jin D K, Xu Z Y, Harrison A P, et al.3D convolutional neural networks with graph refinement for airway segmentation using incomplete data labels[C]//Proceedings of the 8th International Workshop on Machine Learning in Medical Imaging.Quebec City: Springer, 2017: 141-149.[ DOI:10.1007/978-3-319-67389-9_17 http://dx.doi.org/10.1007/978-3-319-67389-9_17 ]
Armato Ⅲ S G, McLennan G, McNitt-Gray M F, et al.Lung image database consortium:developing a resource for the medical imaging research community[J].Radiology, 2004, 232(3):739-748.[DOI:10.1148/radiol.2323032035]
Meng Q E, Kitasaka T, Nimura Y, et al.Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume[J].International Journal of Computer Assisted Radiology and Surgery, 2017, 12(2):245-261.[DOI:10.1007/s11548-016-1492-2]
van Rikxoort E M, Baggerman W, van Ginneken B.Automatic segmentation of the airway tree from thoracic CT scans using a multi-threshold approach[C]//Proceedings of the 2nd International Workshop on Pulmonary Image Analysis.London: Springer, 2009: 341-349.
Sonka M, Park W, Hoffman E A.Rule-based detection of intrathoracic airway trees[J].IEEE Transactions on Medical Imaging, 1996, 15(3):314-326.[DOI:10.1109/42.500140]
Lassen B, van Rikxoort E M, Schmidt M, et al.Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi[J].IEEE Transactions on Medical Imaging, 2013, 32(2):210-222.[DOI:10.1109/TMI.2012. 2219881]
Ceresa M, Artaechevarria X, Munoz-Barrutia A, et al.Automatic leakage detection and recovery for airway tree extraction in chest CT images[C]//2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.Rotterdam: IEEE, 2010: 568-571.[ DOI:10.1109/ISBI.2010.5490282 http://dx.doi.org/10.1109/ISBI.2010.5490282 ]
Nadeem S A, Jin D K, Hoffman E A, et al.An iterative method for airway segmentation using multiscale leakage detection[C]//Proceedings of SPIE 10133, Medical Imaging 2017: Image Processing.Orlando: SPIE, 2017: #1013308.[ DOI:10.1117/12.2254507 http://dx.doi.org/10.1117/12.2254507 ]
Saha P K, Borgefors G, di Baja G S.A survey on skeletonization algorithms and their applications[J].Pattern Recognition Letters, 2016, 76:3-12.[DOI:10.1016/j.patrec.2015.04.006]
Schlathoelter T, Lorenz C, Carlsen I C, et al.Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy[C]//Proceedings of SPIE 4684, Medical Imaging 2002 : Image Processing.San Diego: SPIE, 2002: 103-113.[ DOI:10.1117/12.467061 http://dx.doi.org/10.1117/12.467061 ]
Lee T C, Kashyap R L, Chu C N.Building skeleton models via 3-D medial surface axis thinning algorithms[J].CVGIP:Graphical Models and Image Processing, 1994, 56(6):462-478.[DOI:10.1006/cgip.1994.1042]
Palágyi K, Balogh E, Kuba A, et al.A sequential 3D thinning algorithm and its medical applications[C]//Proceedings of the 17th International Conference on Biennial International Conference on Information Processing in Medical Imaging.Davis: Springer, 2001: 409-415.[ DOI:10.1007/3-540-45729-1_42 http://dx.doi.org/10.1007/3-540-45729-1_42 ]
Ma C M, Sonka M.A fully parallel 3D thinning algorithm and its applications[J].Computer Vision and Image Understanding, 1996, 64(3):420-433.[DOI:10.1006/cviu.1996.0069]
Palágyi K, Tschirren J, Sonka M.Quantitative analysis of intrathoracic airway trees: methods and validation[C]//Proceedings of the 18th International Conference on Biennial International Conference on Information Processing in Medical Imaging. Ambleside: Springer, 2003: 222-233.[DOI: DOI:10.1007/978-3-540-45087-0_19 http://dx.doi.org/10.1007/978-3-540-45087-0_19 ]
Bian Z J, Tan W J, Yang J Z, et al.Accurate airway centerline extraction based on topological thinning using graph-theoretic analysis[J].Bio-Medical Materials and Engineering, 2014, 24(6):3239-3249.[DOI:10.3233/BME-141146]
Gagvani N, Silver D.Parameter controlled skeletonization of three dimensional objects[R].Technical Report CAIP-TR-216, New Jersey: The State University of New Jersey, 1997.
Kiraly A P, Helferty J P, Hoffman E A, et al.Three-dimensional path planning for virtual bronchoscopy[J].IEEE Transactions on Medical Imaging, 2004, 23(11):1365-1379.[DOI:10.1109/TMI.2004.829332]
Smistad E, Elster A C, Lindseth F.GPU-based airway segmentation and centerline extraction for image guided bronchoscopy[C]//Norsk Informatikkonferanse NIK 2012.Trondheim: Akademika Forlag, 2012: 129-140.
Saba O I, Hoffman E A, Reinhardt J M.Maximizing quantitative accuracy of lung airway lumen and wall measures obtained from X-ray CT imaging[J].Journal of Applied Physiology, 2003, 95(3):1063-1075.[DOI:10.1152/japplphysiol.00962.2002]
Lee J, Reeves A P, Fotin S, et al.Human airway measurement from CT images[C]//Proceedings of SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis.San Diego: SPIE, 2008: #691518.[ DOI:10.1117/12.770601 http://dx.doi.org/10.1117/12.770601 ]
Mumcuoglu E U, Prescott J, Baker B N, et al.Image analysis for cystic fibrosis: automatic lung airway wall and vessel measurement on CT images[C]//Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Minneapolis: IEEE, 2009: 3545-3548.[ DOI:10.1109/IEMBS.2009.5332413 http://dx.doi.org/10.1109/IEMBS.2009.5332413 ]
Leader J K, Zheng B, Sciurba F C, et al.Quantification of airway morphometry: the effect of CT acquisition and reconstruction parameters[C]//Proceedings of SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images.San Diego: SPIE, 2007: #65111R.[ DOI:10.1117/12.709940 http://dx.doi.org/10.1117/12.709940 ]
Kiraly A P, Odry B L, Naidich D P, et al.Boundary-specific cost functions for quantitative airway analysis[C]//Proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention.Brisbane: Springer, 2007: 784-791.[ DOI:10.1007/978-3-540-75757-3_95 http://dx.doi.org/10.1007/978-3-540-75757-3_95 ]
Petersen J, Lo P, Nielsen M, et al.Quantitative analysis of airway abnormalities in CT[C]//Proceedings of SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis.San Diego: SPIE, 2010: #76241S.[ DOI:10.1117/12.843937 http://dx.doi.org/10.1117/12.843937 ]
Gu S C, Fuhrman C, Meng X, et al.Computerized identification of airway wall in CT examinations using a 3D active surface evolution approach[J].Medical Image Analysis, 2013, 17(3):283-296.[DOI:10.1016/j.media.2012.11.003]
Fetita C, Ortner M, Brillet P Y, et al.Volumetric quantification of airway wall in CT via collision-free active surface model:application to asthma assessment[J].IEEE Transactions on Medical Imaging, 2014, 33(7):1512-1526.[DOI:10.1109/TMI.2014. 2316115]
Frangi A F, Niessen W J, Vincken K L, et al.Multiscale vessel enhancement filtering[C]//Proceedings of the 1st International Conference on Medical Image Computing and Computer-Assisted Intervention.Cambridge: Springer, 1998: 130-137.[ DOI:10.1007/BFb0056195 http://dx.doi.org/10.1007/BFb0056195 ]
van Dongen E, van Ginneken B.Automatic segmentation of pulmonary vasculature in thoracic CT scans with local thresholding and airway wall removal[C]//2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.Rotterdam: IEEE, 2010: 668-671.[ DOI:10.1109/ISBI.2010.5490088 http://dx.doi.org/10.1109/ISBI.2010.5490088 ]
ÖksüzÏ, Vnay D, Kadıpaşaogǧlu K.Region growing on Frangi vesselness values in 3-D CTA data[C]//Proceedings of the 201321st Signal Processing and Communications Applications Conference.Haspolat: IEEE, 2013: 1-4.[ DOI:10.1109/SIU.2013.6531407 http://dx.doi.org/10.1109/SIU.2013.6531407 ]
Orkisz M, Hoyos M H, Romanello V P, et al.Segmentation of the pulmonary vascular trees in 3D CT images using variational region-growing[J].IRBM, 2014, 35(1):11-19.[DOI:10.1016/j.irbm.2013.12.001]
Sato Y, Nakajima S, Shiraga N, et al.Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images[J].Medical Image Analysis, 1998, 2(2):143-168.[DOI:10.1016/S1361-8415(98)80009-1]
Shikata H, McLennan G, Hoffman E A, et al.Segmentation of pulmonary vascular trees from thoracic 3D CT images[J].Journal of Biomedical Imaging, 2009, 2009:#636240.[DOI:10.1155/2009/636240]
Zhai Z W, Staring M, Stoel B C.Lung vessel segmentation in CT images using graph-cuts[C]//Proceedings of SPIE 9784, Medical Imaging 2016: Image Processing.San Diego: SPIE, 2016: #97842K.[ DOI:10.1117/12.2216827 http://dx.doi.org/10.1117/12.2216827 ]
Lo P, van Ginneken B, de Bruijne M.Vessel tree extraction using locally optimal paths[C]//2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.Rotterdam: IEEE, 2010: 680-683.[ DOI:10.1109/ISBI.2010.5490083 http://dx.doi.org/10.1109/ISBI.2010.5490083 ]
Kaftan J N, Kiraly A P, Bakai A, et al.Fuzzy pulmonary vessel segmentation in contrast enhanced CT data[C]//Proceedings of SPIE 6914, MedicalImaging 2008: Image Processing.San Diego: SPIE, 2008: #69141Q.[ DOI:10.1117/12.768795 http://dx.doi.org/10.1117/12.768795 ]
Agam G, Wu C.Probabilistic modeling based vessel enhancement in thoracic CT scans[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Diego: IEEE, 2005: 649-654.[ DOI:10.1109/CVPR.2005.280 http://dx.doi.org/10.1109/CVPR.2005.280 ]
Zhou C, Chan H P, Sahiner B, et al.Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications[J].Medical Physics, 2007, 34(12):4567-4577.[DOI:10.1118/1.2804558]
Saha P K, Gao Z Y, Alford S K, et al.Topomorphologic separation of fused isointensity objects via multiscale opening:separating arteries and veins in 3-D pulmonary CT[J].IEEE Transactions on Medical Imaging, 2010, 29(3):840-851.[DOI:10.1109/TMI.2009.2038224]
Charbonnier J P, Brink M, Ciompi F, et al.Automatic pulmonary artery-vein separation and classification in computed tomography using tree partitioningand peripheral vessel matching[J].IEEE Transactions on Medical Imaging, 2016, 35(3):882-892.[DOI:10.1109/TMI.2015.2500279]
Buelow T, Wiemker R, Blaffert T, et al.Automatic extraction of the pulmonary artery tree from multi-slice CT data[C]//Proceedings of SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images.San Diego: SPIE, 2005: 730-740.[ DOI:10.1117/12.595286 http://dx.doi.org/10.1117/12.595286 ]
Nishio T, Saita S, Kubo M, et al.Feature calculation for classification algorithm of pulmonary vein and artery[C]//Proceedings of SPIE 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images.San Diego: SPIE, 2006: #61432X.[ DOI:10.1117/12.652948 http://dx.doi.org/10.1117/12.652948 ]
Wiemker R, Bulow T, Blaffert T.Unsupervised extraction of the pulmonary interlobar fissures from high resolution thoracic CT data[J].International Congress Series, 2005, 1281:1121-1126.[DOI:10.1016/j.ics.2005.03.130]
van Rikxoort E M, van Ginneken B, Klik M, et al.Supervised enhancement filters:application to fissure detection in chest CT scans[J].IEEE Transactions on Medical Imaging, 2008, 27(1):1-10.[DOI:10.1109/TMI.2007.900447]
Xiao C Y, Staring M, Wang J, et al.A derivative of stick filter for pulmonary fissure detection in CT images[C]//Proceedings of SPIE 8669, Medical Imaging 2013: Image Processing.Lake Buena Vista: SPIE, 2013: #86690V.[ DOI:10.1117/12.2006566 http://dx.doi.org/10.1117/12.2006566 ]
Wei Q, Hu Y, MacGregor J H, et al.Segmentation of lung lobes in volumetric CT images for surgical planning of treating lung cancer[C]//Proceedings of 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.New York: IEEE, 2006: 4869-4872.[ DOI:10.1109/IEMBS.2006.260551 http://dx.doi.org/10.1109/IEMBS.2006.260551 ]
Pu J T, Leader J K, Zheng B, et al.A computational geometry approach to automated pulmonary fissure segmentation in CT examinations[J].IEEE Transactions on Medical Imaging, 2009, 28(5):710-719.[DOI:10.1109/TMI.2008.2010441]
Wei Q, Hu Y P, MacGregor J H, et al.Automatic recognition of major fissures in human lungs[J].International Journal of Computer Assisted Radiology and Surgery, 2012, 7(1):111-123.[DOI:10.1007/s11548-011-0632-y]
Neto E C, Cortez P C, Cavalcante T S, et al.Supervised enhancement filter applied to fissure detection[M]//Braidot A, Hadad A.VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. Cham: Springer, 2015: 337-340.[ DOI:10.1007/978-3-319-13117-7_87 http://dx.doi.org/10.1007/978-3-319-13117-7_87 ]
Kuhnigk J M, Hahn H, Hindennach M, et al.Lung lobe segmentation by anatomy-guided 3D watershed transform[C]//Proceedings of SPIE 5032, Medical Imaging 2003: Image Processing.San Diego: SPIE, 2003: 1482-1490.[ DOI:10.1117/12.480321 http://dx.doi.org/10.1117/12.480321 ]
van Rikxoort E M, Prokop M, de Hoop B, et al.Automatic segmentation of pulmonary lobes robust against incomplete fissures[J].IEEE Transactions on Medical Imaging, 2010, 29(6):1286-1296.[DOI:10.1109/TMI.2010.2044799]
Zhou X R, Hayashi T, Hara T, et al.Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images[J].Computerized Medical Imaging and Graphics, 2006, 30(5):299-313.[DOI:10.1016/j.compmedimag. 2006.06.002]
Ukil S, Reinhardt J M.Anatomy-guided lung lobe segmentation in X-ray CT images[J].IEEE Transactions on Medical Imaging, 2009, 28(2):202-214.[DOI:10.1109/TMI.2008.929101]
Pu J T, Zheng B, Leader J K, et al.Pulmonary lobe segmentation in CT examinations using implicit surface fitting[J].IEEE Transactions on Medical Imaging, 2009, 28(12):1986-1996.[DOI:10.1109/TMI.2009.2027117]
Ross J C, Estépar R S J, Díaz A, et al.Lung extraction, lobe segmentation and hierarchical region assessment for quantitative analysis on high resolution computed tomography images[C]//Proceedings of the 12th International Conference on Medical Image Computing and Computer-AssistedIntervention.London: Springer, 2009: 690-698.[ DOI:10.1007/978-3-642-04271-3_84 http://dx.doi.org/10.1007/978-3-642-04271-3_84 ]
Shamonin D P, Staring M, Bakker M E, et al.Automatic lung lobe segmentation of COPD patients using iterative B-spline fitting[C]//Proceedings of SPIE 8314, Medical Imaging 2012: Image Processing.San Diego: SPIE, 2012: #83140W.[ DOI:10.1117/12.910869 http://dx.doi.org/10.1117/12.910869 ]
Zhang L, Hoffman E A, Reinhardt J M.Atlas-driven lung lobe segmentation in volumetric X-ray CT images[J].IEEE Transactions on Medical Imaging, 2006, 25(1):1-16.[DOI:10.1109/TMI.2005.859209]
Kuhnigk J M, Dicken V, Zidowitz S, et al.New tools for computer assistance in thoracic CT.Part 1.Functional analysis of lungs, lung lobes, and bronchopulmonary segments[J].Radiographics, 2005, 25(2): 525-536.[ DOI:10.1148/rg.252045070 http://dx.doi.org/10.1148/rg.252045070 ]
Welter S, Stöcker C, Dicken V, et al.Lung segment geometry study:Simulation of largest possible tumours that fit into bronchopulmonary segments[J].The Thoracic and Cardiovascular Surgeon, 2012, 60(2):93-100.[DOI:10.1055/s-0030-1271009]
Stoecker C, Welter S, Moltz J H, et al.Determination of lung segments in computed tomography images using the Euclidean distance to the pulmonary artery[J].Medical Physics, 2013, 40(9):#091912.[DOI:10.1118/1.4818017]
Busayarat S, Zrimec T.Detection of bronchopulmonary segments on high-resolution CT——preliminary results[C]//20th International Symposium on Computer-Based Medical Systems.Maribor: IEEE, 2007: 199-204.[ DOI:10.1109/CBMS.2007.37 http://dx.doi.org/10.1109/CBMS.2007.37 ]
Busayarat S, Zrimec T.Bronchopulmonary segments approximation using anatomical atlas[C]//Proceedings of SPIE 6512, Medical Imaging 2007: Image Processing.San Diego: SPIE, 2007: #65122O.[ DOI:10.1117/12.709651 http://dx.doi.org/10.1117/12.709651 ]
Kumar A, Agarwala S, Dhara A K, et al.Localization of lung fields in HRCT images using a deep convolution neural network[C]//Proceedings of SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis.Houston: SPIE, 2018: #1057535.
Liu X M, Guo S X, Yang B T, et al.Automatic organ segmentation for CT scans based on super-pixel and convolutional neural networks[J].Journal of Digital Imaging, 2018.[DOI:10.1007/s10278-018-0052-4]
Charbonnier J P, van Rikxoort E M, Setio A A A, et al.Improving airway segmentation in computed tomography using leak detection with convolutional networks[J].Medical Image Analysis, 2017, 36:52-60.[DOI:10.1016/j.media. 2016. 11.001]
Tajbakhsh N, Shin J Y, Gurudu S R, et al.Convolutional neural networks for medical image analysis:Full training or fine tuning?[J].IEEE Transactions on Medical Imaging, 2016, 35(5):1299-1312.[DOI:10.1109/TMI.2016.2535302]
相关作者
相关机构
京公网安备11010802024621
