An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing

Ren, He; Zhou, Lingxiao; Liu, Gang; Peng, Xueqing; Shi, Wei; Xu, Huilin; Shan, Fei; Liu, Lei

doi:10.21037/qims.2019.12.02

Cited by 26 publications

(13 citation statements)

References 38 publications

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“…During the image review, the radiologists were blind to the patients' pathological results. The whole tumor area was segmented with a semi-automatic method in Matlab 2018b (Mathworks, Natick, MA, USA), as described below (28,29). One of the two radiologists manually delineated a region of interest (ROI) with an arbitrary shape around the lesion area on the subtraction image.…”

Section: Image Processing and Lesion Segmentationmentioning

confidence: 99%

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

2021

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PurposeTo evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors.Materials and MethodsA total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves.ResultsIn the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808–0.906), 0.860 (95% CI, 0.806–0.904), 0.934 (95% CI, 0.891–0.963), and 0.921 (95% CI, 0.876–0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747–0.908), 0.784 (95% CI, 0.601–0.798), 0.890 (95% CI, 0.806–0.946), and 0.865 (95% CI, 0.777–0.928), respectively.ConclusionThe texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.

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Section: Image Processing and Lesion Segmentationmentioning

confidence: 99%

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

2021

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“…Lee et al (23) and Zheng and Lei (24) are interesting reviews of hand-designed methods for lung nodule segmentation featuring thresholding, region growing, watershed, edge detection and active contours. Other engineered approaches include clustering (25), graph-based methods (26), fractal analysis (27), convexity models (28), vector quantisation (29) and a variety of ad hoc solutions as well as combinations of the above methods (30)(31)(32)(33)(34). Deep learning approaches differ significantly from the conventional ones in that they employ pre-defined architectures [convolutional neural networks (CNN)] which contain a number of parameters the values of which need to be determined by training (35).…”

Section: Original Articlementioning

confidence: 99%

Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT

Bianconi¹,

Fravolini²,

Pizzoli³

et al. 2021

Quant Imaging Med Surg

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Background: Accurate segmentation of pulmonary nodules on computed tomography (CT) scans plays a crucial role in the evaluation and management of patients with suspicion of lung cancer (LC). When performed manually, not only the process requires highly skilled operators, but is also tiresome and timeconsuming. To assist the physician in this task several automated and semi-automated methods have been proposed in the literature. In recent years, in particular, the appearance of deep learning has brought about major advances in the field.Methods: Twenty-four (12 conventional and 12 based on deep learning) semi-automated-'one-click'methods for segmenting pulmonary nodules on CT were evaluated in this study. The experiments were carried out on two datasets: a proprietary one (383 images from a cohort of 111 patients) and a public one (259 images from a cohort of 100). All the patients had a positive transcript for suspect pulmonary nodules. Results:The methods based on deep learning clearly outperformed the conventional ones. The best performance [Sørensen-Dice coefficient (DSC)] in the two datasets was, respectively, 0.853 and 0.763 for the deep learning methods, and 0.761 and 0.704 for the traditional ones.Conclusions: Deep learning is a viable approach for semi-automated segmentation of pulmonary nodules on CT scans.

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“…Nodule segmentation was performed semi-automatically with in-house software (24) and manually reviewed slice by slice by a radiologist with 6 years of experience in chest CT imaging and confirmed by another radiologist with 20 years of experience. A slice example of the nodule segmentation was provided in supplementary figure 1.…”

Section: Radiological Features Extraction and Radiomics Features Extractionmentioning

confidence: 99%

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter

Shi

Peng

et al. 2021

Front. Oncol.

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PurposeTo develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter.Materials and MethodsThis retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts.ResultsFive radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful.ConclusionThis study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.

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An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing

Cited by 26 publications

References 38 publications

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors

Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter

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