HeMIS: Hetero-modal image segmentation

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

Volumn 9901 LNCS, Issue , 2016, Pages 469-477

  Havaei, Mohammad ^a   Guizard, Nicolas ^a   Chapados, Nicolas ^a   Bengio, Yoshua ^b  

^a INSTITUTE FOR RESEARCH IN IMMUNOLOGY AND CANCER   (Canada)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

Convolutional neural networks; Data abstraction; Data imputation; Deep learning; Multi modal; Segmentation

Indexed keywords

MEDICAL IMAGING; NEURAL NETWORKS; VECTOR SPACES;

CONVOLUTIONAL NEURAL NETWORK; DATA ABSTRACTION; DATA IMPUTATION; DEEP LEARNING; MULTI-MODAL;

IMAGE SEGMENTATION;

EID: 84996504008     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46723-8_54     Document Type: Conference Paper

References (14)

1. Brosch,T.,Yoo,Y.,Tang,L.Y.W.,Li,D.K.B.,Traboulsee,A.,Tam,R.: Deep convolutional encoder networks for multiple sclerosis lesion segmentation. In: Navab,N.,Hornegger,J.,Wells,W.M.,Frangi,A.F. (eds.) MICCAI 2015. LNCS,vol. 9351,pp. 3-11. Springer,Heidelberg (2015). doi:10.1007/978-3-319-24574-4_1
2. Geremia,E.,Menze,B.H.,Ayache,N.: Spatially adaptive random forests,pp. 1344-1347 (2013)
3. Goodfellow,I.,Bengio,Y.,Courville,A.: Deep learning. MIT Press,Cambridge (2016)
4. Guizard,N.,Coupé,P.,Fonov,V.S.,Manjón,J.V.,Arnold,D.L.,Collins,D.L.: Rotation-invariant multi-contrast non-local means for ms lesion segmentation. NeuroImage Clin. 8,376-389 (2015)
5. Havaei,M.,Davy,A.,Warde-Farley,D.,Biard,A.,Courville,A.,Bengio,Y.,Pal,C.,Jodoin,P.M.,Larochelle,H.: Brain tumor segmentation with deep neural networks. arXiv preprint (2015). arXiv:1505.03540
6. Hofmann,M.,Steinke,F.,Scheel,V.,Charpiat,G.,et al.: MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. J. Nucl. Med. 49(11),1875-1883 (2008)
7. Hor,S.,Moradi,M.: Scandent Tree: a random forest learning method for incomplete multimodal datasets. In: Navab,N.,Hornegger,J.,Wells,W.M.,Frangi,A.F. (eds.) MICCAI 2015. LNCS,vol. 9349,pp. 694-701. Springer,Heidelberg (2015). doi:10.1007/978-3-319-24553-9
8. Menze,B.,Jakab,A.,Bauer,S.,Kalpathy-Cramer,J.,Farahani,K.,Kirby,J.E.A.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE TMI 34(10),1993-2024 (2015)
9. Souplet,J.,Lebrun,C.,Ayache,N.,Malandain,G.: An automatic segmentation of T2-FLAIR multiple sclerosis lesions,7 2008
10. Styner,M.,Lee,J.,Chin,B.,Chin,M.,Commowick,O.,Tran,H.,Markovic-Plese,S.,Jewells,V.,Warfield,S.: 3D segmentation in the clinic: A grand challenge ii: Ms lesion segmentation. MIDAS 2008,1-6 (2008)
11. Tulder,G.,Bruijne,M.: Why does synthesized data improve multi-sequence classification? In: Navab,N.,Hornegger,J.,Wells,W.M.,Frangi,A.F. (eds.) MICCAI 2015. LNCS,vol. 9349,pp. 531-538. Springer,Heidelberg (2015). doi:10.1007/ 978-3-319-24553-9_65
12. Tustison,N.J.,Shrinidhi,K.,Wintermark,M.,Durst,C.R.,Kandel,B.M.,Gee,J.C.,Grossman,M.C.,Avants,B.B.: Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13 (2),209-225 (2015)
13. Van Buuren,S.: Flexible imputation of missing data. CRC Press,Boca Raton (2012)
14. Zhao,L.,Wu,W.,Corso,J.J.: Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories. In: Mori,K.,Sakuma,I.,Sato,Y.,Barillot,C.,Navab,N. (eds.) MICCAI 2013. LNCS,vol. 8151,pp. 567-575. Springer,Heidelberg (2013). doi:10.1007/978-3-642-40760-4_71