Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

He, Baochun; Huang, Cheng; Sharp, G; Zhou, S. Kevin; Hu, Qingmao; Fang, Chihua; Fan, Yingfang; Jia, Fucang

doi:10.1118/1.4946817

Cited by 31 publications

(25 citation statements)

References 23 publications

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“…Flow chart of the study. (A) The portal vein contrast-enhanced computed tomography (CECT) image was used as input, and the liver was segmented by homemade segmentation software(22) and manually corrected. The tumor ROI was contoured by a radiologist with more than 10 years of experience.…”

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confidence: 99%

Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma

Peng

et al. 2020

Front. Oncol.

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Objective: This study aimed to build and evaluate a radiomics feature-based model for the preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma. Methods: A total of 145 patients were retrospectively included in the study pool, and the patients were divided randomly into two independent cohorts with a ratio of 7:3 (training cohort: n = 101, validation cohort: n = 44). For a pilot study of this predictive model another 18 patients were recruited into this study. A total of 1,231 computed tomography (CT) image features of the liver parenchyma without tumors were extracted from portalphase CT images. A least absolute shrinkage and selection operator (LASSO) logistic regression was applied to build a radiomics score (Rad-score) model. Afterwards, a nomogram, including Rad-score as well as other clinicopathological risk factors, was established with a multivariate logistic regression model. The discrimination efficacy, calibration efficacy, and clinical utility value of the nomogram were evaluated. Results: The Rad-score scoring model could predict MVI with the area under the curve (AUC) of 0.637 (95% CI, 0.516-0.758) in the training cohort as well as of 0.583 (95% CI, 0.395-0.770) in the validation cohort; however, the aforementioned discriminative approach could not completely outperform those existing predictors (alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin). The individual predictive nomogram which included the Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin showed a better discrimination efficacy with AUC of 0.865 (95% CI, 0.786-0.944), which was higher than the conventional methods' AUCs (nomogram vs Rad-score, alpha fetoprotein, neutrophilic granulocyte, and preoperative hemoglobin at P < 0.001, P = 0.025, P < 0.001, and P = 0.001, respectively). When applied to the validation cohort, the nomogram discrimination efficacy was still outbalanced those above mentioned three remaining methods (AUC: 0.705; 95% CI, 0.537-0.874). The calibration curves of this proposed method showed a satisfying

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confidence: 99%

Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma

Peng

et al. 2020

Front. Oncol.

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“…The recognition accuracy is more than 70% [18]. In the same year, Ning et al used machine learning ID3 [19], classification and regression tree [20], and AdaBoost three different algorithms for feature extraction of their performance [21]. The AdaBoost performed well in these algorithms [22].…”

Section: B Skin Disease Image Recognition Based On Machine Learningmentioning

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Deep Learning in Skin Disease Image Recognition: A Review

Wang

et al. 2020

IEEE Access

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“…Automatic contour propagation using deformable image registration (DIR) was reported for 4DCT‐based respiratory motion assessment,27, 28, 29 CT‐based longitudinal adaptive evaluation,30, 31, 32 and cone‐beam CT for setup with liver matching 33. Other automatic image segmentation approaches were reported,34, 35 including model‐based method. Recently, automatic OAR contouring on 2D cine or 3D MR images were reported to facilitate MR‐based planning or MR‐guided radiotherapy 36, 37, 38, 39.…”

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Evaluation of automatic contour propagation in T2‐weighted 4DMRI for normal‐tissue motion assessment using internal organ‐at‐risk volume (IRV)

Zhang

Markova

García

et al. 2018

J Applied Clin Med Phys

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PurposeThe purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory‐correlated navigator‐triggered four‐dimensional magnetic resonance imaging (RC‐4DMRI) for calculation of internal organ‐at‐risk volume (IRV) to account for intra‐fractional OAR motion.Methods and MaterialsT2‐weighted RC‐4DMRI images were of 10 volunteers acquired and reconstructed using an internal navigator‐echo surrogate and concurrent external bellows under an IRB‐approved protocol. Four major OARs (lungs, heart, liver, and stomach) were delineated in the 10‐phase 4DMRI. Two manual‐contour sets were delineated by two clinical personnel and two automatic‐contour sets were propagated using free‐form deformable image registration. The OAR volume variation within the 10‐phase cycle was assessed and the IRV was calculated as the union of all OAR contours. The OAR contour similarity between the navigator‐triggered and bellows‐rebinned 4DMRI was compared. A total of 2400 contours were compared to the most probable ground truth with a 95% confidence level (S95) in similarity, sensitivity, and specificity using the simultaneous truth and performance level estimation (STAPLE) algorithm.ResultsVisual inspection of automatically propagated contours finds that approximately 5–10% require manual correction. The similarity, sensitivity, and specificity between manual and automatic contours are indistinguishable (P > 0.05). The Jaccard similarity indexes are 0.92 ± 0.02 (lungs), 0.89 ± 0.03 (heart), 0.92 ± 0.02 (liver), and 0.83 ± 0.04 (stomach). Volume variations within the breathing cycle are small for the heart (2.6 ± 1.5%), liver (1.2 ± 0.6%), and stomach (2.6 ± 0.8%), whereas the IRV is much larger than the OAR volume by: 20.3 ± 8.6% (heart), 24.0 ± 8.6% (liver), and 47.6 ± 20.2% (stomach). The Jaccard index is higher in navigator‐triggered than bellows‐rebinned 4DMRI by 4% (P < 0.05), due to the higher image quality of navigator‐based 4DMRI.ConclusionAutomatic and manual OAR contours from Navigator‐triggered 4DMRI are not statistically distinguishable. The navigator‐triggered 4DMRI image provides higher contour quality than bellows‐rebinned 4DMRI. The IRVs are 20–50% larger than OAR volumes and should be considered in dose estimation.

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Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

Cited by 31 publications

References 23 publications

Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma

Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma

Deep Learning in Skin Disease Image Recognition: A Review

Evaluation of automatic contour propagation in T2‐weighted 4DMRI for normal‐tissue motion assessment using internal organ‐at‐risk volume (IRV)

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