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Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)

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 

Notes

Acknowledgements

We would like to thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Peregrine high performance computing cluster.

Disclosure of Conflicts of Interest.

The authors have no relevant conflicts of interest to disclose.

Ethical Approval.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Financial Support.

This work is part of the research program STRaTeGy with project number 14929, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). This study was financed by the Dutch Cancer Society, POINTING project, grant 10034.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Nuclear Medicine and Molecular Imaging, Medical Imaging CenterUniversity of Groningen, University Medical Center GroningenGroningenThe Netherlands
  2. 2.Faculty of Medicine and Life SciencesHasselt UniversityDiepenbeekBelgium
  3. 3.Department of Nuclear MedicineZiekenhuis Oost LimburgGenkBelgium
  4. 4.Department of Radiology and Nuclear MedicineVU University Medical CenterAmsterdamThe Netherlands
  5. 5.Department of Respiratory MedicineZiekenhuis Oost LimburgGenkBelgium
  6. 6.Department of Respiratory MedicineAZ Vesalius HospitalTongerenBelgium
  7. 7.Institute for Materials Research (IMO) - Division ChemistryHasselt UniversityDiepenbeekBelgium

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