Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images

Zhou, Xiangrong; Hayashi, Tetsutaro; Hara, Takeshi; Fujita, Hiroshi; Yokoyama, Ryuichi; Kiryu, Takuji; Hoshi, Hiroaki

doi:10.1016/j.compmedimag.2006.06.002

Cited by 81 publications

(77 citation statements)

References 13 publications

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“…27 This would allow registration of the preoperative CT to either an initial intraoperative CBCT (I 0 ) or perhaps directly to the intraoperative CBCT of the deflated lung. The segmentation methods used to define the lung surfaces and airways currently require manual placement of seeds, and although a number of automated methods have been developed for CT, [28][29][30] none have been yet adapted for C-arm CBCT or the intraoperative workflow. One particular segmentation challenge is in obtaining an accurate boundary at the medial surface, and the sensitivity of registration accuracy to such segmentation errors is a subject of future work.…”

Section: Discussionmentioning

confidence: 99%

“…As the main focus of the current work is the registration problem (not the segmentation problem), in this context the segmented structures are regarded as input to the algorithm. Exploiting the high image gradients at tissue-air boundaries, semiautomated region-growing methods demonstrated reasonable performance for initial investigation of the model-driven approach; however, more advanced segmentation methods [28][29][30] exist and will be investigated in future work, as discussed in Sec. IV.…”

Section: Iia1 Segmentation Of Model Structuresmentioning

confidence: 99%

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Deformable registration of the inflated and deflated lung in cone‐beam CT‐guided thoracic surgery: Initial investigation of a combined model‐ and image‐driven approach

et al. 2012

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Purpose: Surgical resection is the preferred modality for curative treatment of early stage lung cancer, but localization of small tumors (<10 mm diameter) during surgery presents a major challenge that is likely to increase as more early-stage disease is detected incidentally and in low-dose CT screening. To overcome the difficulty of manual localization (fingers inserted through intercostal ports) and the cost, logistics, and morbidity of preoperative tagging (coil or dye placement under CT-fluoroscopy), the authors propose the use of intraoperative cone-beam CT (CBCT) and deformable image registration to guide targeting of small tumors in video-assisted thoracic surgery (VATS). A novel algorithm is reported for registration of the lung from its inflated state (prior to pleural breach) to the deflated state (during resection) to localize surgical targets and adjacent critical anatomy. Methods: The registration approach geometrically resolves images of the inflated and deflated lung using a coarse model-driven stage followed by a finer image-driven stage. The model-driven stage uses image features derived from the lung surfaces and airways: triangular surface meshes are morphed to capture bulk motion; concurrently, the airways generate graph structures from which corresponding nodes are identified. Interpolation of the sparse motion fields computed from the bounding surface and interior airways provides a 3D motion field that coarsely registers the lung and initializes the subsequent image-driven stage. The image-driven stage employs an intensity-corrected, symmetric form of the Demons method. The algorithm was validated over 12 datasets, obtained from porcine specimen experiments emulating CBCT-guided VATS. Geometric accuracy was quantified in terms of target registration error (TRE) in anatomical targets throughout the lung, and normalized crosscorrelation. Variations of the algorithm were investigated to study the behavior of the model-and image-driven stages by modifying individual algorithmic steps and examining the effect in comparison to the nominal process. Results: The combined model-and image-driven registration process demonstrated accuracy consistent with the requirements of minimally invasive VATS in both target localization (∼3-5 mm within the target wedge) and critical structure avoidance (∼1-2 mm). The model-driven stage initialized the registration to within a median TRE of 1.9 mm (95% confidence interval (CI) maximum = 5.0 mm), while the subsequent image-driven stage yielded higher accuracy localization with 0.6 mm median TRE (95% CI maximum = 4.1 mm). The variations assessing the individual algorithmic steps elucidated the role of each step and in some cases identified opportunities for further simplification and improvement in computational speed. Conclusions:The initial studies show the proposed registration method to successfully register CBCT images of the inflated and deflated lung. Accuracy appears sufficient to localize the target and adjacent critical anatomy within ∼1-2 mm and guide ...

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Section: Discussionmentioning

confidence: 99%

Section: Iia1 Segmentation Of Model Structuresmentioning

confidence: 99%

Deformable registration of the inflated and deflated lung in cone‐beam CT‐guided thoracic surgery: Initial investigation of a combined model‐ and image‐driven approach

et al. 2012

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“…Due to the large number of segmentation methods, we have categorized these methods into five intuitive groups for easier comprehension: thresholding-based, stochastic, region-based, contour-based, and learning-based methods, as shown in Figure 3. www.ijarai.thesai.org Thresholding is a simple segmentation technique that converts a gray-level image into a binary image by defining all pixels greater than some value to be foreground and all other pixels are considered as background [7] [99]. In [17], the authors separate nodule candidates from CT images using mathematical morphology and grey level thresholding.…”

Section: ) 2d-based Approachesmentioning

confidence: 99%

“…Zhou et al [99], Wang et al [85] and Retico et al [73] implemented a histogram-based thresholding to segregate the lung region from the adjacent structure.…”

Section: ) 2d-based Approachesmentioning

confidence: 99%

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Mahersia¹,

Zaroug²,

Gabralla³

2015

ijarai

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Abstract-Lung nodules are potential manifestations of lung cancer, and their early detection facilitates early treatment and improves patient's chances for survival. For this reason, CAD systems for lung cancer have been proposed in several studies. All these works involved mainly three steps to detect the pulmonary nodule: preprocessing, segmentation of the lung and classification of the nodule candidates. This paper overviews the current state-of-the-art regarding all the approaches and techniques that have been investigated in the literature. It also provides a comparison of the performance of the existing approaches.

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“…Numerical lung analysis: Lung segmentation; volumetric analysis; densitometry; fractal dimension estimation Lung segmentation: Methods of lung segmentation are well established [8][9][10]. The lung segmentation approach used in this study is described in detail in ref.…”

Section: Computed Tomography Imagingmentioning

confidence: 99%

Detailed investigation of volumetric analysis, densitometry, and fractal geometry based concepts in computer aided medical diagnoses

Obert¹,

Stamm²,

Goerner³

et al. 2016

Fractal Geometry and Nonlinear Anal in Med and Biol

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Purpose: Automated image analysis tools have the potential to improve the objectivity of the diagnostic process. The study and improvement of the numerical methodologies behind these tools is, therefore, crucial. Volumetric, densitometric, and fractal analysis concepts were, thus, explored in the setting of computed tomography (CT) imaging of different lung morphologies. Material and methods:Thoracic CT scans were acquired for five sheep prior to and after smoke inhalation injury. Software was developed to segment the lungs from the digital image data and to estimate the morphometric parameters "volume", "Hounsfield unit-density" (HU), and "fractal dimension". These parameters were estimated for each scan, once from the complete dataset, covering the entire a range of -1000 to 399 HU, and once for 28 consecutive data subsets, with a width of 50 HU each. T-test statistics were used to investigate group differences "before" and "after" smoke inhalation, based on a 0.05 significance level.Results: For the complete data set, group differentiation into "before" and "after" smoke in-halation was feasible only with volumetric analysis. Analysis of 28 smaller HU subsets, on the other hand, allowed group differentiation with all three morphometric parameters. Conclusions:The analysis of small HU subsets can be helpful in differentiating groups and may be a useful approach for many image analysis projects.

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Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images

Cited by 81 publications

References 13 publications

Deformable registration of the inflated and deflated lung in cone‐beam CT‐guided thoracic surgery: Initial investigation of a combined model‐ and image‐driven approach

Deformable registration of the inflated and deflated lung in cone‐beam CT‐guided thoracic surgery: Initial investigation of a combined model‐ and image‐driven approach

Lung Cancer Detection on CT Scan Images: A Review on the Analysis Techniques

Detailed investigation of volumetric analysis, densitometry, and fractal geometry based concepts in computer aided medical diagnoses

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