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Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography
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Summary :
This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.4 pp.864-868
- Publication Date
- 2013/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.864
- Type of Manuscript
- Special Section LETTER (Special Section on Medical Imaging)
- Category
- Medical Image Processing
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Akinobu SHIMIZU, Takuya NARIHIRA, Hidefumi KOBATAKE, Daisuke FURUKAWA, Shigeru NAWANO, Kenji SHINOZAKI, "Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 4, pp. 864-868, April 2013, doi: 10.1587/transinf.E96.D.864.
Abstract: This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.864/_p
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@ARTICLE{e96-d_4_864,
author={Akinobu SHIMIZU, Takuya NARIHIRA, Hidefumi KOBATAKE, Daisuke FURUKAWA, Shigeru NAWANO, Kenji SHINOZAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography},
year={2013},
volume={E96-D},
number={4},
pages={864-868},
abstract={This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.},
keywords={},
doi={10.1587/transinf.E96.D.864},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography
T2 - IEICE TRANSACTIONS on Information
SP - 864
EP - 868
AU - Akinobu SHIMIZU
AU - Takuya NARIHIRA
AU - Hidefumi KOBATAKE
AU - Daisuke FURUKAWA
AU - Shigeru NAWANO
AU - Kenji SHINOZAKI
PY - 2013
DO - 10.1587/transinf.E96.D.864
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2013
AB - This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boost and extends the loss functions to improve performance. Five segmentation algorithms trained by the ensemble learning algorithm with different loss functions are compared in terms of error rate and Jaccard Index between the extracted regions and true ones.
ER -
English
