Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37654
Title: Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images
Authors: Pfaehler, E
MESOTTEN, Liesbet 
Kramer, G
THOMEER, Michiel 
VANHOVE, Karolien 
de Jong , Johan
ADRIAENSENS, Peter 
Hoekstra, OS
Boellaard, R
Editors: Papiez, BW
Namburete, Ail
Yaqub, M
Noble, JA
Issue Date: 2020
Publisher: Papiez, BW; Namburete, AIL; Yaqub, M; Noble, JA; MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, SPRINGER INTERNATIONAL PUBLISHING AG, p. 3-14
Source: MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, Papiez, BW; Namburete, AIL; Yaqub, M; Noble, JA; MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, SPRINGER INTERNATIONAL PUBLISHING AG, p. 3-14, p. 3 -14
Series/Report: Communications in Computer and Information Science
Abstract: In oncology, Positron Emission Tomography (PET) is frequently performed for cancer staging and treatment monitoring. Metabolic active tumor volume (MATV) as well as total MATV (TMATV - including primary tumor, lymph nodes and metastasis) derived from PET images have been identified as prognostic factor or for evaluating treatment efficacy in cancer patients. To this end a segmentation approach with high precision and repeatability is important. Moreover, to derive TMATV, a reliable segmentation of the primary tumor as well as all metastasis is essential. However, the implementation of a repeatable and accurate segmentation algorithm remains a challenge. In this work, we propose an artificial intelligence based segmentation method based on textural features (TF) extracted from the PET image. From a large number of textural features, the most important features for the segmentation task were selected. The selected features are used for training a random forest classifier to identify voxels as tumor or background. The algorithm is trained, validated and tested using a lung cancer PET/CT dataset and, additionally, applied on a fully independent test-retest dataset. The approach is especially designed for accurate and repeatable segmentation of primary tumors and metastasis in order to derive TMATV. The segmentation results are compared with conventional segmentation approaches in terms of accuracy and repeatability. In summary, the TF segmentation proposed in this study provided better repeatability and accuracy than conventional segmentation approaches. Moreover, segmentations were accurate for both primary tumors and metastasis and the proposed algorithm is therefore a good candidate for PET tumor segmentation.
Keywords: Tumor segmentation;PET;Textural feature segmentation;Repeatability;Artificial intelligence
Document URI: http://hdl.handle.net/1942/37654
ISBN: 978-3-030-52790-7
978-3-030-52791-4
DOI: 10.1007/978-3-030-52791-4_1
ISI #: 000770412700001
Rights: Springer Nature Switzerland AG 2020. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Category: C1
Type: Proceedings Paper
Validations: ecoom 2023
Appears in Collections:Research publications

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