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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
A semi-automated toolkit for analysis of liver cancer treatment response using Perfusion CT.
In: (International MICCAI Workshop on Computational and Clinical Challenges in Abdominal Imaging). Berlin [u.a.]: Springer, 2014. 23-32 (Lect. Notes Comput. Sc. ; 8676)
Delineation of hepatic tumours is challenging in CT due to limited inherent tissue contrast, leading to significant intra-/interobserver variability. Perfusion CT (pCT) allows quantitative assessment of enhancement patterns in normal and abnormal liver. This study aims to develop a semi-automated perfusion analysis toolkit that classifies hepatic tissue based on perfusion-derived parameters. pCT data from patients with hepatic metastases were used in this study. Tumour motion was minimized through image registration; perfusion parameters were derived and then employed in the training of a machine learning algorithm used to classify hepatic tissue. This method was found to deliver promising results for 10 data sets, with recorded sensitivity and specificity of the tissue classification in the ranges of 0.92–0.99 and 0.98– 0.99 respectively. This semi-automated method could be used to analyze response over the treatment course, as it is not based on intensity values.
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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Hepatic Tumor ; Motion ; Perfusion Ct ; Registration ; Tissue Classification
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
International MICCAI Workshop on Computational and Clinical Challenges in Abdominal Imaging
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 8676,
Seiten: 23-32
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
Publikationen
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)