Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

By Adhish Prasoon, Kersten Petersen, Christian Igel, François Lauze, Erik Bjørnager Dam, Mads Nielsen. 2013

604 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism, Rheumatology

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In Computer Vision and Pattern Recognition

In Endocrinology, Diabetes and Metabolism

In Rheumatology

Mohammad Mustafa Taye

Citations in the corpus

Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks (2016, background): “A combination of three CNNs is suggested in [15].”
A survey on deep learning in medical image analysis (2017, methodology): “Prentasic et al. (2016) Blood vessel segmentation in OCT angiography; simple CNN, segmentation of several capillary networks”
Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2016, methodology): “To incorporate 3D contextual information, multiple works used 2D CNNs on three orthogonal 2D patches (Prasoon et al. (2013); Roth et al. (2014); Lyksborg et al. (2015)).”
Deep Learning in Medical Imaging: General Overview (2017, methodology): “These include approaches for the segmentation of the lungs (41), tumors and other structures in the brain (42, 43), biological cells and membranes (27, 44), tibial cartilage (45), bone tissue (46), and cell mitosis (47).”
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? (2016, methodology): “CNNs have recently been used for carotid intima-media thickness measurement in ultrasound images [22], pancreas segmentation in CT images [23], brain tumor segmentation in magnetic resonance imaging (MRI) scans [24], multimodality isointense infant brain image segmentation [25], neuronal membrane se”
Quantitative susceptibility mapping using deep neural network: QSMnet (2018, methodology): “Deep neural networks have also been applied for medical image reconstruction such as X-ray, CT, PET, and MRI (Prasoon et al., 2013; Teramoto et al., 2016; Yang et al., 2018; Zichuan et al., 2017; Park et al., 2016).”
Deep neural networks for anatomical brain segmentation (2015, background): “elegans embryos [17], biological neuron membrane [5], tibial cartilage [19], bone tissue [4] and cell mitosis [6], amongst others.”
Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images (2017, methodology): “2015), orthogonal planes (Prasoon et al. 2013; Roth et al. 2014) or multi-view planes (Setio et al.”
VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation (2016, background): “, axial, coronal and sagittal) [25, 27] as input to make up complementary spatial information.”
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges (2019, background): “[54, 60, 65] are inspired by the fact that 2.5D has the richer spatial information of neighboring pixels with less computational costs than 3D. Generally, they involve extracting three orthogonal 2D patches in the XY , YZ , and XZ planes, respectively, as shown in Fig.”
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields (2016, methodology): “[20,8,23,21,19,12].”
Deep Learning and Its Applications in Biomedicine (2018, methodology): “…have been demonstrated in segmenting the left ventricle of the heart from the MR data [112], the pancreas through computed tomography (CT) [113], tibial cartilage through magnetic resonance imaging (MRI) [114], the prostate through MRI [115], and the hippocampus through MR brain images [116,117].”
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge (2016, methodology): “The fusion of the different ConvNets is performed using the late fusion method (Prasoon et al. (2013); Karpathy”
A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations (2014, methodology): “Studies that apply deep learning and CNNs to medical imaging applications also show promise, e.g. (Prasoon et al., 2013), and classifying digital pathology (Cirean et al., 2013).”
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2017, methodology): “For example, [26], [27] used a CNN to classify each pixel into the target object or the background by taking a local patch centered on that pixel as input.”