Page 313 - Read Online
P. 313
Page 12 of 12 Torabinia et al. Mini-invasive Surg 2021;5:32 https://dx.doi.org/10.20517/2574-1225.2021.63
ACM international conference on Multimedia (MM '14); New York, USA. 2014.
38. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, LeCun Y. Overfeat: Integrated recognition, localization and detection using
convolutional networks. 2013.
39. Chandan G, Jain A, Jain H, Mohana. Real time object detection and tracking using deep learning and OpenCV. 2018 International
Conference on Inventive Research in Computing Applications (ICIRCA); 2018 Jul 11-12; Coimbatore, India. 2018.
40. Rajchl M, Lee MC, Oktay O, et al. DeepCut: object segmentation from bounding box annotations using convolutional neural networks.
IEEE Trans Med Imaging 2017;36:674-83. DOI PubMed PMC
41. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J,
Wells W, Frangi A, editors. Medical image computing and computer-assisted intervention - MICCAI 2015. Cham: Springer; 2015. p.
234-41.
42. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell
2017;39:640-51. DOI PubMed
43. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse
annotation. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W, editors. Medical image computing and computer-assisted
intervention - MICCAI 2016. Cham: Springer; 2016. p. 424-32.
44. Milletari F, Navab N, Ahmadi S. V-Net: fully convolutional neural networks for volumetric medical image segmentation. 2016 Fourth
International Conference on 3D Vision (3DV); 2016 Oct 25-28; Stanford, USA. 2016.
45. Li X, Chen H, Qi X, Dou Q, Fu CW, Heng PA. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from
CT volumes. IEEE Trans Med Imaging 2018;37:2663-74. DOI PubMed
46. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. UNet++: a nested U-Net architecture for medical image segmentation. In:
Stoyanov D, Taylor Z, Carneiro G, Syeda-mahmood T, Martel A, Maier-hein L, Tavares JMR, Bradley A, Papa JP, Belagiannis V,
Nascimento JC, Lu Z, Conjeti S, Moradi M, Greenspan H, Madabhushi A, editors. Deep learning in medical image analysis and
multimodal learning for clinical decision support. Cham: Springer International Publishing; 2018. p. 3-11.
47. Zhang J, Jin Y, Xu J, Xu X, Zhang Y. Mdu-net: multi-scale densely connected u-net for biomedical image segmentation. 2018.
48. Jin Q, Meng Z, Pham TD, et al. DUNet: a deformable network for retinal vessel segmentation. Knowledge-Based Systems
2019;178:149-62. DOI
49. Jin Q, Meng Z, Sun C, Cui H, Su R. RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans. Front
Bioeng Biotechnol 2020;8:605132. DOI PubMed PMC
50. Dolz J, Ben Ayed I, Desrosiers C. Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities. In:
Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, van Walsum T, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic
brain injuries. Cham: Springer; 2018. p. 271-82.
51. Xiao W, Duan X, Lin Y, et al. Distinct proteome remodeling of industrial saccharomyces cerevisiae in response to prolonged thermal
stress or transient heat shock. J Proteome Res 2018;17:1812-25. DOI PubMed
52. Isensee F, Petersen J, Klein A, et al. nnu-net: self-adapting framework for u-net-based medical image segmentation. 2018.
53. Kingma DP, Ba J. Adam: a method for stochastic optimization. Computer Science 2014.
54. Fukumoto Y, Tsutsui H, Tsuchihashi M, Masumoto A, Takeshita A. The incidence and risk factors of cholesterol embolization
syndrome, a complication of cardiac catheterization: a prospective study. J Am Coll Cardiol 2003;42:211-6. DOI PubMed
55. Loffroy R, Guiu B, Cercueil JP, Krausé D. Endovascular therapeutic embolisation: an overview of occluding agents and their effects
on embolised tissues. Curr Vasc Pharmacol 2009;7:250-63. DOI PubMed
56. Dice LR. Measures of the amount of ecologic association between species. Ecology 1945;26:297-302. DOI
57. Sra J, Krum D, Choudhuri I, et al. Identifying the third dimension in 2D fluoroscopy to create 3D cardiac maps. JCI Insight
2016;1:e90453. DOI PubMed PMC
58. Liu J, Al'Aref SJ, Singh G, et al. An augmented reality system for image guidance of transcatheter procedures for structural heart
disease. PLoS One 2019;14:e0219174. DOI PubMed PMC