Supervised recursive segmentation of volumetric CT images for 3D reconstruction of lung and vessel tree

Li, Xuanping; Wang, Xue; Dai, Yixiang

doi:10.1016/j.cmpb.2015.08.014

Cited by 23 publications

(18 citation statements)

References 42 publications

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“…Hence, by comparing the centroid values of the segmented clusters from one slice to other, vessels can be eliminated from candidate nodules. 24,25 The algorithmic steps used for centroid-shift analysis were as follows:…”

Section: D Centroid-shift Analysis To Classify Nodules From Vesselsmentioning

confidence: 99%

Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives

Krishnamurthy

Narasimhan²,

Umamaheswari³

2015

Proc Inst Mech Eng H

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The three-dimensional analysis on lung computed tomography scan was carried out in this study to detect the malignant lung nodules. An automatic three-dimensional segmentation algorithm proposed here efficiently segmented the tissue clusters (nodules) inside the lung. However, an automatic morphological region-grow segmentation algorithm that was implemented to segment the well-circumscribed nodules present inside the lung did not segment the juxta-pleural nodule present on the inner surface of wall of the lung. A novel edge bridge and fill technique is proposed in this article to segment the juxta-pleural and pleural-tail nodules accurately. The centroid shift of each candidate nodule was computed. The nodules with more centroid shift in the consecutive slices were eliminated since malignant nodule's resultant position did not usually deviate. The three-dimensional shape variation and edge sharp analyses were performed to reduce the false positives and to classify the malignant nodules. The change in area and equivalent diameter was more for malignant nodules in the consecutive slices and the malignant nodules showed a sharp edge. Segmentation was followed by three-dimensional centroid, shape and edge analysis which was carried out on a lung computed tomography database of 20 patient with 25 malignant nodules. The algorithms proposed in this article precisely detected 22 malignant nodules and failed to detect 3 with a sensitivity of 88%. Furthermore, this algorithm correctly eliminated 216 tissue clusters that were initially segmented as nodules; however, 41 non-malignant tissue clusters were detected as malignant nodules. Therefore, the false positive of this algorithm was 2.05 per patient.

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Section: D Centroid-shift Analysis To Classify Nodules From Vesselsmentioning

confidence: 99%

Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives

Krishnamurthy

Narasimhan²,

Umamaheswari³

2015

Proc Inst Mech Eng H

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“…There are multiple algorithms and software packages capable of segmenting lung tissue, the liver, the aorta, the spine, and multiple organs . However, these algorithms can be more computationally expensive, less robust, and not fully automated .…”

Section: Discussionmentioning

confidence: 99%

Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images

2017

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Patient-specific organ HUs can be measured from clinical datasets. The algorithm that was developed can be run on both contrast-enhanced and non-contrast-enhanced clinical datasets. The method can be applied to automatically extract image HU-contrast characteristics of clinical CT images, not captured in phantom data, whereby enabling quantification and optimization of image quality and contrast administration.

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“… Contour correction (a) Ideal contour correction result, (b) Correction result with traditional methods, (c) Partition rule [40] used in our lung contour correction approach…”

Section: Methodsmentioning

confidence: 99%

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

2020

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Accurately segmenting lungs from CT images is a fundamental step for quantitative analysis of lung diseases. However, it is still a challenging task because of some interferential factors, such as juxta-pleural nodules, pulmonary inflammation, as well as individual anatomical varieties. In this study, with the combination of a superpixel approach and a hybrid model composed of convolutional neural network and random forest (CNN-RF), the authors propose a novel algorithm to segment lungs from CT images in an automatic and accurate fashion. The authors' lung segmentation covers three main stages: image preprocessing, lung segmenting and segmentation refining. A lung CT image denoised with a fractional-order grey similarity approach is first segmented to a set of superpixels, and the CNN-RF model is then employed to classify the superpixels and identify lungs from the CT image. The segmentation result is further refined by separating the left and right lungs, eliminating trachea, and correcting lung contours. Experiments show that their algorithm can generate more accurate lung segmentation results with 94.98% Jaccard's index and 97.99% Dice similarity coefficient, compared with ground truths, and it achieved better results compared with several feature-based machine learning techniques and current methods on lung segmentation.

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Supervised recursive segmentation of volumetric CT images for 3D reconstruction of lung and vessel tree

Cited by 23 publications

References 42 publications

Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives

Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives

Patient‐specific quantification of image quality: An automated technique for measuring the distribution of organ Hounsfield units in clinical chest CT images

Novel superpixel‐based algorithm for segmenting lung images via convolutional neural network and random forest

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