An artificial agent for anatomical landmark detection in medical images

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

Volumn 9902 LNCS, Issue , 2016, Pages 229-237

  Ghesu, Florin C ^a,b   Georgescu, Bogdan ^a   Mansi, Tommaso ^a   Neumann, Dominik ^a   Hornegger, Joachim ^b   Comaniciu, Dorin ^a  

^a Medical Imaging Technologies   (United States)

^b UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; FEATURE EXTRACTION; LEARNING SYSTEMS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; REINFORCEMENT LEARNING; ULTRASONIC APPLICATIONS;

ANATOMICAL LANDMARKS; ANATOMICAL STRUCTURES; APPEARANCE MODELING; APPROXIMATION SCHEME; FEATURE COMPUTATION; HIERARCHICAL FEATURE EXTRACTION; OBJECT APPEARANCE; SEARCH STRATEGIES;

COMPUTERIZED TOMOGRAPHY;

EID: 84996551865     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46726-9_27     Document Type: Conference Paper

References (11)

1. Bengio,Y.,Courville,A.C.,Vincent,P.: Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives. CoRR abs/1206.5538 (2012)
2. Caicedo,J.C.,Lazebnik,S.: Active object localization with deep reinforcement learning. In: IEEE ICCV,pp. 2488–2496 (2015)
3. Ghesu,F.C.,Krubasik,E.,Georgescu,B.,Singh,V.,Zheng,Y.,Hornegger,J.,Comaniciu,D.: Marginal space deep learning: efficient architecture for volumetric image parsing. IEEE TMI 35(5),1217–1228 (2016)
4. Lin,L.J.: Reinforcement Learning for Robots Using Neural Networks. Ph.D. thesis,Carnegie Mellon University,Pittsburgh,PA,USA (1992)
5. Lu,X.,Georgescu,B.,Jolly,M.-P.,Guehring,J.,Young,A.,Cowan,B.,Littmann,A.,Comaniciu,D.: Cardiac anchoring in MRI through context modeling. In: Jiang,T.,Navab,N.,Pluim,J.P.W.,Viergever,M.A. (eds.) MICCAI 2010,Part I. LNCS,vol. 6361,pp. 383–390. Springer,Heidelberg (2010)
6. Lu,X.,Jolly,M.-P.: Discriminative context modeling using auxiliary markers for LV landmark detection from a single MR image. In: Camara,O.,Mansi,T.,Pop,M.,Rhode,K.,Sermesant,M.,Young,A. (eds.) STACOM 2012. LNCS,vol. 7746,pp. 105–114. Springer,Heidelberg (2013)
7. Mnih,V.,Kavukcuoglu,K.,Silver,D.,Rusu,A.A.,Veness,J.,Bellemare,M.G.,Graves,A.,Riedmiller,M.,Fidjeland,A.K.,Ostrovski,G.,Petersen,S.,Beattie,C.,Sadik,A.,Antonoglou,I.,King,H.,Kumaran,D.,Wierstra,D.,Legg,S.,Hassabis,D.: Human-level control through deep reinforcement learning. Nature 518(7540),529–533 (2015)
8. Russell,S.J.,Norvig,P.: Artificial Intelligence: A Modern Approach,2nd edn. Pearson Education,Upper Saddle River (2003)
9. Sutton,R.S.,Barto,A.G.: Introduction to Reinforcement Learning,1st edn. MIT Press,Cambridge (1998)
10. Watkins,C.J.C.H.,Dayan,P.: Q-learning. Mach. Learn. 8(3),279–292 (1992)
11. Zheng,Y.,Liu,D.,Georgescu,B.,Nguyen,H.,Comaniciu,D.: 3D deep learning for efficient and robust landmark detection in volumetric data. In: Navab,N.,Hornegger,J.,Wells,W.M.,Frangi,A.F. (eds.) MICCAI 2015. LNCS,vol. 9349,pp. 565–572. Springer,Heidelberg (2015). doi:10.1007/978-3-319-24553-9_69