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, and W-Net obtained the best result for segmentation and lesion detection. It also outperformed traditional machine learning methods such as random forest classifier (RF), -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.
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Publication date: Available online 4 January 2018 Source: European Journal of Radiology Author(s): Peiyao Zhang, Jing Wang, Qin Xu, Zhen...
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Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182, Butyric Acid from Probiotic Staphyloco...
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Abstract The development of focused ion beam-scanning electron microscopy (FIB-SEM) techniques has allowed high-resolution 3D imaging of n...
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Vol.48 No.2 from #AlexandrosSfakianakis via Alexandros G.Sfakianakis on Inoreader http://ift.tt/1S2Z7n2 via IFTTT
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