W-Net for Whole-Body Bone Lesion Detection on \(^{68}\)Ga-Pentixafor PET/CT Imaging of Multiple Myeloma Patients

  • Lina Xu
  • Giles Tetteh
  • Mona Mustafa
  • Jana Lipkova
  • Yu Zhao
  • Marie Bieth
  • Patrick Christ
  • Marie Piraud
  • Bjoern Menze
  • Kuangyu Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10555)

Abstract

The assessment of bone lesion is crucial for the diagnostic and therapeutic planning of multiple myeloma (MM). \(^{68}\)Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, the whole-body detection of dozens of lesions on hybrid imaging is tedious and error-prone. In this paper, we adopt a cascaded convolutional neural networks (CNN) to form a W-shaped architecture (W-Net). This deep learning method leverages multimodal information for lesion detection. The first part of W-Net extracts skeleton from CT scan and the second part detect and segment lesions. The network was tested on 12 \(^{68}\)Ga-Pentixafor PET/CT scans of MM patients using 3-folder cross validation. The preliminary results showed that W-Net can automatically learn features from multimodal imaging for MM bone lesion detection. The proof-of-concept study encouraged further development of deep learning approach for MM lesion detection with increased number of subjects.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lina Xu
    • 1
    • 3
  • Giles Tetteh
    • 1
    • 2
  • Mona Mustafa
    • 3
  • Jana Lipkova
    • 1
    • 2
  • Yu Zhao
    • 1
    • 2
  • Marie Bieth
    • 1
    • 2
  • Patrick Christ
    • 1
    • 2
  • Marie Piraud
    • 1
    • 2
  • Bjoern Menze
    • 1
    • 2
  • Kuangyu Shi
    • 3
  1. 1.Department of Computer ScienceTU MünchenMunichGermany
  2. 2.Institute of Medical EngineeringTU MünchenMunichGermany
  3. 3.Department of Nuclear Medicine, Klinikum Rechts der IsarTU MünchenMunichGermany

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