3D U-net: Learning dense volumetric segmentation from sparse annotation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9901 LNCS, Issue , 2016, Pages 424-432

  Çiçek, Özgün ^a   Abdulkadir, Ahmed ^a,b   Lienkamp, Soeren S ^a,b   Brox, Thomas ^a   Ronneberger, Olaf ^a,c  

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF FREIBURG   (Germany)

^c DEEPMIND   (United Kingdom)

Author keywords

3D; Biomedical volumetric image segmentation; Convolutional neural networks; Fully automated; Semi automated; Sparse annotation; Xenopus kidney

Indexed keywords

AUTOMATION; BIOLOGICAL ORGANS; COMPLEX NETWORKS; MEDICAL IMAGING; NEURAL NETWORKS;

CONVOLUTIONAL NEURAL NETWORK; FULLY AUTOMATED; SEMI-AUTOMATED; SPARSE ANNOTATION; VOLUMETRIC IMAGE SEGMENTATION; XENOPUS;

IMAGE SEGMENTATION;

EID: 84996483314     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46723-8_49     Document Type: Conference Paper

References (14)

1. Emmenlauer,M.,Ronneberger,O.,Ponti,A.,Schwarb,P.,Griffa,A.,Filippi,A.,Nitschke,R.,Driever,W.,Burkhardt,H.: Xuvtools: free,fast and reliable stitching of large 3D datasets. J. Microscopy 233(1),42-60 (2009)
2. Fedorov,A.,Beichel,R.,Kalpathy-Cramer,J.,Finet,J.,Fillion-Robin,J.C.,Pujol,S.,Bauer,C.,Jennings,D.,Fennessy,F.,Sonka,M.,et al.: 3D slicer as an image computing platform for the quantitative imaging network. J. Magn. Reson. Imaging 30(9),1323-1341 (2012)
3. Hariharan,B.,Arbeláez,P.,Girshick,R.,Malik,J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceeding CVPR,pp. 447-456 (2015)
4. Ioffe,S.,Szegedy,C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167 (2015)
5. Jia,Y.,Shelhamer,E.,Donahue,J.,Karayev,S.,Long,J.,Girshick,R.,Guadarrama,S.,Darrell,T.: Caffe: Convolutional architecture for fast feature embedding. In: Proceeding ACMMM,pp. 675-678 (2014)
6. Kleesiek,J.,Urban,G.,Hubert,A.,Schwarz,D.,Maier-Hein,K.,Bendszus,M.,Biller,A.: Deep mri brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage (2016)
7. Lienkamp,S.,Ganner,A.,Boehlke,C.,Schmidt,T.,Arnold,S.J.,Schäfer,T.,Romaker,D.,Schuler,J.,Hoff,S.,Powelske,C.,Eifler,A.,Krönig,C.,Bullerkotte,A.,Nitschke,R.,Kuehn,E.W.,Kim,E.,Burkhardt,H.,Brox,T.,Ronneberger,O.,Gloy,J.,Walz,G.: Inversin relays frizzled-8 signals to promote proximal pronephros development. PNAS 107(47),20388-20393 (2010)
8. Long,J.,Shelhamer,E.,Darrell,T.: Fully convolutional networks for semantic segmentation. In: Proceeding CVPR,pp. 3431-3440 (2015)
9. Milletari,F.,Ahmadi,S.,Kroll,C.,Plate,A.,Rozanski,V.E.,Maiostre,J.,Levin,J.,Dietrich,O.,Ertl-Wagner,B.,Bötzel,K.,Navab,N.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. CoRR abs/1601.07014 (2016)
10. Nieuwkoop,P.,Faber,J.: Normal Table of Xenopus laevis (Daudin). Garland,New York (1994)
11. Ronneberger,O.,Fischer,P.,Brox,T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab,N.,Hornegger,J.,Wells,W.M.,Frangi,A.F. (eds.) MICCAI 2015. LNCS,vol. 9351,pp. 234-241. Springer,Heidelberg (2015). doi:10.1007/978-3-319-24574-4
12. Seyedhosseini,M.,Sajjadi,M.,Tasdizen,T.: Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks. In: Proceeding ICCV,pp. 2168-2175 (2013)
13. Szegedy,C.,Vanhoucke,V.,Ioffe,S.,Shlens,J.,Wojna,Z.: Rethinking the inception architecture for computer vision. CoRR abs/1512.00567 (2015)
14. Tran,D.,Bourdev,L.D.,Fergus,R.,Torresani,L.,Paluri,M.: Deep end2end voxel2voxel prediction. CoRR abs/1511.06681 (2015)