Deep vessel tracking: A generalized probabilistic approach via deep learning

A survey on deep learning in medical image analysis

“…Kisilev et al (2016) used a completely different approach and predicted categorical BI-RADS descriptors for breast lesions. In their work they focused on three descriptors used in mammography: shape, margin, and density, Fu et al (2016a) Blood vessel segmentation; extending the approach by Fu et al (2016b) by reformulating CRF as RNN Mahapatra et al (2016) Image quality assessment; classification output using CNN-based features combined with the output using saliency maps Maninis et al (2016) Segmentation of blood vessels and optic disk; VGG-19 network extended with specialized layers for each segmentation task Wu et al (2016) Blood vessel segmentation; patch-based CNN followed by mapping PCA solution of last layer feature maps to full segmentation Zilly et al (2017) Segmentation of the optic disk and the optic cup; simple CNN with filters sequentially learned using boosting Color fundus images: detection of abnormalities and diseases Chen et al (2015d) Glaucoma detection; end-to-end CNN, the input is a patch centered at the optic disk Abràmoff et al (2016) Diabetic retinopathy detection; end-to-end CNN, outperforms traditional method, evaluated on a public dataset Burlina et al (2016) Age-related macular degeneration detection; uses overfeat pretrained network for feature extraction van Grinsven et al (2016) Hemorrhage detection; CNN dynamically trained using selective data sampling to perform hard negative mining Gulshan et al (2016) Diabetic retinopathy detection; Inception network, performance comparable to a panel of seven certified ophthalmologists Hard exudate detection; end-to-end CNN combined with the outputs of traditional classifiers for detection of landmarks Worrall et al (2016) Retinopathy of prematurity detection; fine-tuned ImageNet trained GoogLeNet, feature map visualization to highlight disease Work in other imaging modalities Gao et al (2015) Cataract classification in slit lamp images; CNN followed by a set of recursive neural networks to extract higher order features Schlegl et al (2015) Fluid segmentation in OCT; weakly supervised CNN improved with semantic descriptors from clinical reports Blood vessel segmentation in OCT angiography; simple CNN, segmentation of several capillary networks where each have their own class label. The system was fed with the image data and region proposals and predicts the correct label for each descriptor (e.g.…”

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

“…The tracking exploits a multi-dimensional assignment problem, which is solved with rank-1 tensor approximation. Similarly, a deep CNN is trained for estimating local retinal vessel probability via principal component analysis and nearest neighbor search in [52]. The resulting vessel map is exploited to extract the entire connected tree with a probabilistic tracking approach.…”

“…V-A) Oliveira et al [22] 2011 Liver CT Goceri et al [23] 2017 Liver MRI Bruyninckx et al [24] 2010 Liver CT Bruyninckx et al [25] 2009 Lung CT Asad et al [26] 2017 Retina CFP Mapayi et al [27] 2015 Retina CFP Sreejini et al [28] 2015 Retina CFP Cinsdikici et al [29] 2009 Retina CFP Al-Rawi et al [30] 2007 Retina CFP Hanaoka et al [31] 2015 Brain MRA Supervised machine learning Sironi et al [32] 2014 Brain Microscopy (Sec. V-B) Merkow et al [33] 2016 Cardiovascular and Lung CT and MRI Sankaran et al [34] 2016 Coronary CTA Schaap et al [35] 2011 Coronary CTA Zheng et al [36] 2011 Coronary CT Nekovei et al [37] 1995 Coronary CT Smistad et al [38] 2016 Femoral region, Carotid US Chu et al [39] 2016 Liver X-ray fluoroscopic Orlando et al [40] 2017 Retina CFP Dasgupta et al [41] 2017 Retina CFP Mo et al [42] 2017 Retina CFP Lahiri et al [43] 2017 Retina CFP Annunziata et al [44] 2016 Retina Microscopy Fu et al [45] 2016 Retina CFP Luo et al [46] 2016 Retina CFP Liskowski et al [47] 2016 Retina CFP Li et al [48] 2016 Retina CFP Javidi et al [49] 2016 Retina CFP Maninis et al [50] 2016 Retina CFP Prentasvic et al [51] 2016 Retina CT Wu et al [52] 2016 Retina CFP Annunziata et al [53] 2015 Retina Microscopy Annunziata et al [54] 2015 Retina Microscopy Vega et al [55] 2015 Retina CFP Wang et al [56] 2015 Retina CFP Fraz et al [57] 2014 Retina CFP Ganin et al [58] 2014 Retina CFP...…”

Deep Learning in Cardiology

“…Fu et al [174] train a CNN on DRIVE and STARE databases to generate the vessel probability maps and then they employed a fully connected CRF to combine the discriminative vessel probability maps and long-range interactions between pixels. In [175] the authors used a CNN to learn the features and a PCA-based nearest neighbor search utilized to estimate the local structure distribution. Besides demonstrating good results they argue that it is important for CNN to incorporate information regarding the tree structure in terms of accuracy.…”