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

Kazuya TAKAGI; Satoshi KONDO; Kensuke NAKAMURA; Mitsuyoshi TAKIGUCHI

doi:10.1587/transinf.2013EDP7464

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

Kazuya TAKAGI, Satoshi KONDO, Kensuke NAKAMURA, Mitsuyoshi TAKIGUCHI

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One 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.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.11 pp.2947-2954

Publication Date: 2014/11/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2013EDP7464

Type of Manuscript: PAPER

Category: Biological Engineering

Authors

Kazuya TAKAGI
  Panasonic Healthcare Co., Ltd.
Satoshi KONDO
  Panasonic Healthcare Co., Ltd.
Kensuke NAKAMURA
  Hokkaido University
Mitsuyoshi TAKIGUCHI
  Hokkaido University

Keyword

contrast-enhanced ultrasound, computer-aided diagnosis, lesion classification, Adaboost, Haar-like

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Kazuya TAKAGI, Satoshi KONDO, Kensuke NAKAMURA, Mitsuyoshi TAKIGUCHI, "Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 11, pp. 2947-2954, November 2014, doi: 10.1587/transinf.2013EDP7464.
Abstract: One 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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2013EDP7464/_p

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@ARTICLE{e97-d_11_2947,
author={Kazuya TAKAGI, Satoshi KONDO, Kensuke NAKAMURA, Mitsuyoshi TAKIGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images},
year={2014},
volume={E97-D},
number={11},
pages={2947-2954},
abstract={One 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.},
keywords={},
doi={10.1587/transinf.2013EDP7464},
ISSN={1745-1361},
month={November},}

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TY - JOUR
TI - Lesion Type Classification by Applying Machine-Learning Technique to Contrast-Enhanced Ultrasound Images
T2 - IEICE TRANSACTIONS on Information
SP - 2947
EP - 2954
AU - Kazuya TAKAGI
AU - Satoshi KONDO
AU - Kensuke NAKAMURA
AU - Mitsuyoshi TAKIGUCHI
PY - 2014
DO - 10.1587/transinf.2013EDP7464
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2014
AB - One 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.
ER -