2018
DOI: 10.1038/s41598-018-34817-6
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Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

Abstract: Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjec… Show more

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Cited by 145 publications
(88 citation statements)
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“…In order to segment 3D data, it is common to process data as 2D slices and then combine the 2D segmentation maps into a 3D map, since 3D fCNNs are significantly larger in terms of trainable parameters and as a result require significantly larger amounts of data. Nevertheless, these obstacles can be overcome, and there are successful applications of 3D fCNNs in radiology, eg, V‐Net for prostate segmentation from MRI, 3D U‐Net for segmentation of the proximal femur for assessing osteoporosis, and brain glioma segmentation…”
Section: Deep Learning In Radiology: State Of the Artmentioning
confidence: 99%
“…In order to segment 3D data, it is common to process data as 2D slices and then combine the 2D segmentation maps into a 3D map, since 3D fCNNs are significantly larger in terms of trainable parameters and as a result require significantly larger amounts of data. Nevertheless, these obstacles can be overcome, and there are successful applications of 3D fCNNs in radiology, eg, V‐Net for prostate segmentation from MRI, 3D U‐Net for segmentation of the proximal femur for assessing osteoporosis, and brain glioma segmentation…”
Section: Deep Learning In Radiology: State Of the Artmentioning
confidence: 99%
“…They demonstrated the potential of CNNs and outperformed the then state-of-the-art k-Nearest Neighbor classification method. This has motivated many researchers to use CNNs for various medical segmentation tasks, such as the segmentation of brain tissue [24][25][26], prostate [27], bone [28,29], and tumors [30][31][32][33] in MR images. Furthermore, multiple studies have been conducted on the segmentation of kidneys [34] and the pancreas [35][36][37] in CT scans.…”
Section: Related Workmentioning
confidence: 99%
“…Among all the deep learning models, CNN gives effective performance for image segmentation, prediction, and classification. Two-dimensional CNNs (2D-CNNs) and 3D-CNNs [ 28 , 29 , 30 , 31 , 32 , 33 ] are both used for bone age segmentation.…”
Section: Introductionmentioning
confidence: 99%