Paper Info
Title
W-Net for Whole-Body Bone Lesion Detection on ^68 Ga-Pentixafor PET/CT Imaging of Multiple Myeloma Patients.
Abstract
The assessment of bone lesion is crucial for the diagnostic and therapeutic planning of multiple myeloma (MM). Ga-68-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.
Year
DOI
Venue
2017
10.1007/978-3-319-67564-0_3
Lecture Notes in Computer Science
DocType
Volume
ISSN
Conference
10555
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
10
Name
Order
Citations
PageRank
Lina Xu110.70
Giles Tetteh2123.61
Mona Mustafa300.34
Jana Lipková4435.64
Yu Zhao500.34
Marie Bieth632.10
Patrick Ferdinand Christ700.34
Marie Piraud882.17
Bjoern H. Menze9103280.31
Kuangyu Shi10368.12