Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Sugimoto, Katsutoshi; Shiraishi, Junji; Moriyasu, Fuminori; Doi, Kunio

doi:10.4329/wjr.v2.i6.215

Cited by 25 publications

(16 citation statements)

References 47 publications

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“…Unlike the study of Sugimoto et al [7], in which the authors developed two CAD schemes using CEUS, one based on physicians' subjective classification and the other based on quantitative analysis that uses artificial neural networks and decision trees in order to differentiate FLLs, we focused mainly on the malignant/benign fast discrimination of a FLL. In Sugimoto et al study [7] 137 nodules were evaluated: 74 HCCs [23 welldifferentiated (w-HCC), 36 moderately differentiated (m-HCC) and 15 poorly differentiated (p-HCC)], 33 liver metastasis and 30 hemangiomas, classifying them into eight patterns according to the enhancement: (1) absent; (2) dotted; (3) peripheral rim like; (4) peripheral nodular; (5) central with spoke wheel-shape; (6) diffuse heterogeneous; (7) diffuse homogeneous; and (8) others.…”

Section: Discussionmentioning

confidence: 99%

“…In Sugimoto et al study [7] 137 nodules were evaluated: 74 HCCs [23 welldifferentiated (w-HCC), 36 moderately differentiated (m-HCC) and 15 poorly differentiated (p-HCC)], 33 liver metastasis and 30 hemangiomas, classifying them into eight patterns according to the enhancement: (1) absent; (2) dotted; (3) peripheral rim like; (4) peripheral nodular; (5) central with spoke wheel-shape; (6) diffuse heterogeneous; (7) diffuse homogeneous; and (8) others. Four artificial neuronal networks (ANN) were used afterwards as decisions models for the evaluators.…”

Section: Discussionmentioning

confidence: 99%

“…The second scheme that Sugimoto used [7] is based on a micro-flow imaging technique (MFI; Toshiba Medical Systems Co., Otawara, Japan) using high mechanical index disruptive flash frames [22]. Through this technique essential details about the lesions' vessels had been obtained.…”

Section: Discussionmentioning

confidence: 99%

“…There are only few studies regarding the utility of a CAD system for FLLs diagnosis by CEUS. Sugimoto et al [7] proposed two schemes for a CAD classifier of FLLs using CEUS, with encouraging results.…”

Section: Introductionmentioning

confidence: 99%

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Is Contrast Enhanced Ultrasonography a useful tool in a beginner’s hand? How much can a Computer Assisted Diagnosis prototype help in characterizing the malignancy of focal liver lesions?

et al. 2017

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Aim: Contrast enhanced ultrasound (CEUS) improved the characterization of focal liver lesions (FLLs), but is an operatordependent method. The goal of this paper was to test a computer assisted diagnosis (CAD) prototype and to see its benefit in assisting a beginner in the evaluation of FLLs.Material and method: Our cohort included 97 good quality CEUS videos[34% hepatocellular carcinomas (HCC), 12.3% hypervascular metastases (HiperM), 11.3% hypovascular metastases (HipoM), 24.7% hemangiomas (HMG), 17.5% focal nodular hyperplasia (FNH)] that were used to develop a CAD prototype based on an algorithm that tested a binary decision based classifier. Two young medical doctors (1 year CEUS experience), two experts and the CAD prototype, reevaluated 50 FLLs CEUS videos (diagnosis of benign vs. malignant) first blinded to clinical data, in order to evaluate the diagnostic gap beginner vs. expert. Results: The CAD classifier managed a 75.2% overall (benign vs. malignant) correct classification rate. The overall classification rates for the evaluators, before and after clinical data were: first beginner-78%; 94%; second beginner-82%; 96%; first expert-94%; 100%; second expert-96%; 98%. For both beginners, the malignant vs. benign diagnosis significantly improved after knowing the clinical data (p=0.005; p=0,008). The expert was better than the beginner (p=0.04) and better than the CAD (p=0.001). CAD in addition to the beginner can reach the expert diagnosis. Conclusions: The most frequent lesions misdiagnosed at CEUS were FNH and HCC. The CAD prototype is a good comparing tool for a beginner operator that can be developed to assist the diagnosis. In order to increase the classification rate, the CAD system for FLL in CEUS must integrate the clinical data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Is Contrast Enhanced Ultrasonography a useful tool in a beginner’s hand? How much can a Computer Assisted Diagnosis prototype help in characterizing the malignancy of focal liver lesions?

et al. 2017

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“…This causes difficulty in establishing clear criteria for classifying lesions, and why it is said that the diagnostic results for CEUS depend on the reading skills of the physician. We believe that a computer-aided diagnosis can help the physician's diagnosis [5], [6]. There are several quantitative approaches which use a time intensity curve (TIC) to classify lesions with CEUS [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images

Takagi

Kondo

Nakamura

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYOne of the major applications of contrast-enhanced ultrasound (CEUS) is lesion classification. After contrast agents are administered, it is possible to identify a lesion type from its enhancement pattern. However, CEUS image reading is not easy because there are various types of enhancement patterns even for the same type of lesion, and clear classification criteria have not yet been defined. Some studies have used conventional time intensity curves (TICs), which show the vessel dynamics of a lesion. It is possible to predict lesion type from the TIC parameters, such as the coefficients obtained by curve fitting, peak intensity, flow rate and time to peak. However, these parameters are not always provide sufficient accuracy. In this paper, we prepare 1D Haar-like features which describe intensity changes in a TIC and adopt the Adaboost machine learning technique, which eases understanding of which features are useful. Hyperparameters of weak classifiers, e.g., the step size of a Haar-like filter length and threshold for output of the filter, are optimized by searching for those parameters that give the best accuracy. We evaluate the proposed method using 36 focal splenic lesions in canines 16 of which were benign and 20 malignant. The accuracies were 91.7% (33/36) when inspected by an experienced veterinarian, 75.0% (27/36) by linear discriminant analysis (LDA) using conventional three TIC parameters: time to peak, area under curve and peak intensity, and 91.7% (33/36) using our proposed method. McNemar testing shows the p-value to be less than 0.05 between the proposed method and LDA. This result shows the statistical significance of differences between the proposed method and the conventional TIC analysis method using LDA.

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Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

et al. 2019

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Objectives-To develop and validate a proof-of-concept convolutional neural network (CNN)based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.Methods-A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set.

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Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Cited by 25 publications

References 47 publications

Is Contrast Enhanced Ultrasonography a useful tool in a beginner’s hand? How much can a Computer Assisted Diagnosis prototype help in characterizing the malignancy of focal liver lesions?

Is Contrast Enhanced Ultrasonography a useful tool in a beginner’s hand? How much can a Computer Assisted Diagnosis prototype help in characterizing the malignancy of focal liver lesions?

Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images

Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

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