Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network

Hua Ma; Pierre Ambrosini; Theo van Walsum

doi:10.1007/978-3-319-66179-7_52

Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network

Hua Ma, Pierre Ambrosini, Theo van Walsum

Research output: Chapter/Conference proceeding › Chapter › Academic

8 Citations (Scopus)

Original language	Undefined/Unknown
Title of host publication	Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017 (Lecture Notes in Computer Science, vol. 10435)
Editors	Maxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D. Louis Collins, Simon Duchesne
Place of Publication	[S.l.]
Publisher	Springer-Verlag
Pages	453-461
Number of pages	9
ISBN (Print)	9783319661797
DOIs	https://doi.org/10.1007/978-3-319-66179-7_52
Publication status	Published - 2017

Research programs

EMC NIHES-03-30-01

Access to Document

10.1007/978-3-319-66179-7_52

Cite this

Ma, H., Ambrosini, P., & van Walsum, T. (2017). Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network. In Maxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D. Louis Collins, & Simon Duchesne (Eds.), Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017 (Lecture Notes in Computer Science, vol. 10435) (pp. 453-461). Springer-Verlag. https://doi.org/10.1007/978-3-319-66179-7_52

Ma, Hua ; Ambrosini, Pierre ; van Walsum, Theo. / Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network. Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017 (Lecture Notes in Computer Science, vol. 10435). editor / Maxime Descoteaux ; Lena Maier-Hein ; Alfred Franz ; Pierre Jannin ; D. Louis Collins ; Simon Duchesne. [S.l.] : Springer-Verlag, 2017. pp. 453-461

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Ma, H, Ambrosini, P & van Walsum, T 2017, Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network. in Maxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D. Louis Collins & Simon Duchesne (eds), Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017 (Lecture Notes in Computer Science, vol. 10435). Springer-Verlag, [S.l.], pp. 453-461. https://doi.org/10.1007/978-3-319-66179-7_52

Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network. / Ma, Hua; Ambrosini, Pierre; van Walsum, Theo.
Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017 (Lecture Notes in Computer Science, vol. 10435). ed. / Maxime Descoteaux; Lena Maier-Hein; Alfred Franz; Pierre Jannin; D. Louis Collins; Simon Duchesne. [S.l.]: Springer-Verlag, 2017. p. 453-461.

Research output: Chapter/Conference proceeding › Chapter › Academic

TY - CHAP

T1 - Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network

AU - Ma, Hua

AU - Ambrosini, Pierre

AU - van Walsum, Theo

PY - 2017

Y1 - 2017

U2 - 10.1007/978-3-319-66179-7_52

DO - 10.1007/978-3-319-66179-7_52

M3 - Chapter

SN - 9783319661797

SP - 453

EP - 461

BT - Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017 (Lecture Notes in Computer Science, vol. 10435)

A2 - Maxime Descoteaux, null

A2 - Lena Maier-Hein, null

A2 - Alfred Franz, null

A2 - Pierre Jannin, null

A2 - D. Louis Collins, null

A2 - Simon Duchesne, null

PB - Springer-Verlag

CY - [S.l.]

ER -

Ma H, Ambrosini P, van Walsum T. Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network. In Maxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D. Louis Collins, Simon Duchesne, editors, Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2017 (Lecture Notes in Computer Science, vol. 10435). [S.l.]: Springer-Verlag. 2017. p. 453-461 doi: 10.1007/978-3-319-66179-7_52