Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography

Akinobu SHIMIZU; Takuya NARIHIRA; Hidefumi KOBATAKE; Daisuke FURUKAWA; Shigeru NAWANO; Kenji SHINOZAKI

doi:10.1587/transinf.E96.D.864

Special Section on Medical Imaging

Ensemble Learning Based Segmentation of Metastatic Liver Tumours in Contrast-Enhanced Computed Tomography

Akinobu SHIMIZU, Takuya NARIHIRA, Hidefumi KOBATAKE, Daisuke FURUKAWA, Shigeru NAWANO, Kenji SHINOZAKI

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Keywords: CT image, liver tumour, segmentation, ensemble learning, U-boost

JOURNAL FREE ACCESS

2013 Volume E96.D Issue 4 Pages 864-868

DOI https://doi.org/10.1587/transinf.E96.D.864

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Abstract

This paper presents an ensemble learning algorithm for liver tumour segmentation from a CT volume in the form of U-Boostand 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.

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