Journal of Hepatology

2012

DOI: 10.1016/s0168-8278(12)60766-1

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753 Computer Aided Differentiation Model for Automatic Classification of Focal Liver Lesions Based on Contrast-Enhanced Ultrasonography (Ceus) Time Intensity Curve (Tic) Analysis

Costin Teodor Streba¹,

Larisa Săndulescu²,

Cristin Constantin Vere³

et al.

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Artificial Intelligence and Automatic Image Interpretation in Modern Medicine

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et al. 2017

Translational Bioinformatics and Its Application

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Artificial Intelligence and Automatic Image Interpretation in Modern Medicine

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²

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³

et al. 2017

Translational Bioinformatics and Its Application

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Diagnosis System for Hepatocellular Carcinoma Based on Fractal Dimension of Morphometric Elements Integrated in an Artificial Neural Network

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²

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³

et al. 2014

BioMed Research International

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Background and Aims. Hepatocellular carcinoma (HCC) remains a leading cause of death by cancer worldwide. Computerized diagnosis systems relying on novel imaging markers gained significant importance in recent years. Our aim was to integrate a novel morphometric measurement—the fractal dimension (FD)—into an artificial neural network (ANN) designed to diagnose HCC. Material and Methods. The study included 21 HCC and 28 liver metastases (LM) patients scheduled for surgery. We performed hematoxylin staining for cell nuclei and CD31/34 immunostaining for vascular elements. We captured digital images and used an in-house application to segment elements of interest; FDs were calculated and fed to an ANN which classified them as malignant or benign, further identifying HCC and LM cases. Results. User intervention corrected segmentation errors and fractal dimensions were calculated. ANNs correctly classified 947/1050 HCC images (90.2%), 1021/1050 normal tissue images (97.23%), 1215/1400 LM (86.78%), and 1372/1400 normal tissues (98%). We obtained excellent interobserver agreement between human operators and the system. Conclusion. We successfully implemented FD as a morphometric marker in a decision system, an ensemble of ANNs designed to differentiate histological images of normal parenchyma from malignancy and classify HCCs and LMs.

Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors

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et al. 2012

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Neural network analysis of contrast-enhanced ultrasonography - obtained TICs seems a promising field of development for future techniques, providing fast and reliable diagnostic aid for the clinician.

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