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- Title
Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on <sup>68</sup>Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.
- Authors
Lina Xu; Tetteh, Giles; Lipkova, Jana; Yu Zhao; Hongwei Li; Christ, Patrick; Piraud, Marie; Buck, Andreas; Kuangyu Shi; Menze, Bjoern H.
- Abstract
The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner. Two convolutional neural networks (CNNs), V-Net and W-Net, were adopted to segment and detect the lesions. The feasibility of deep learning for lesion detection on 68Ga-Pentixafor PET/CT was first verified on digital phantoms generated using realistic PET simulation methods. Then the proposed methods were evaluated on real 68Ga-Pentixafor PET/CT scans of MM patients. The preliminary results showed that deep learning method can leverage multimodal information for spatial feature representation, andW-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), k-Nearest Neighbors (k-NN), and support vector machine (SVM). The proof-of-concept study encourages further development of deep learning approach for MM lesion detection in population study.
- Publication
Contrast Media & Molecular Imaging, 2018, Vol 2018, p1
- ISSN
1555-4309
- Publication type
Article
- DOI
10.1155/2018/2391925