A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast‐enhanced ultrasound

• Published in: Medical physics (Lancaster) Vol. 42; no. 7; pp. 3948 - 3959
• Main Authors: Gatos, Ilias, Tsantis, Stavros, Spiliopoulos, Stavros, Skouroliakou, Aikaterini, Theotokas, Ioannis, Zoumpoulis, Pavlos, Hazle, John D., Kagadis, George C.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.07.2015
• Subjects: Adult; Aged; Area Under Curve; Biological material, e.g. blood, urine; Haemocytometers; biomedical ultrasonics; classification; Computed tomography; Contrast; Contrast Media; contrast‐enhanced ultrasound; Diagnosis using ultrasonic, sonic or infrasonic waves; Digital computing or data processing equipment or methods, specially adapted for specific applications; feature extraction; Female; Flow visualization; focal liver lesions; Humans; Image analysis; image classification; Image data processing or generation, in general; Image Interpretation, Computer-Assisted - methods; image segmentation; image sequences; In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines; Inference methods or devices; Integral transforms; liver; Liver - diagnostic imaging; Liver Neoplasms - diagnostic imaging; Magnetic resonance imaging; Male; Markov processes; Medical image contrast; medical image processing; Medical image segmentation; Middle Aged; random processes; ROC Curve; Segmentation; Support Vector Machine; support vector machines; time intensity curves; Ultrasonographic imaging; Ultrasonography; video signal processing; Wavelet Analysis; wavelet transforms; Wavelets; Young Adult
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1118/1.4921753

Purpose: Detect and classify focal liver lesions (FLLs) from contrast‐enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm. Methods: The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents’ behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model. Results: With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame‐subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively. Conclusions: The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.