X-ray in-depth decomposition: Revealing the latent structures

Albarqouni S, Fotouhi J, Navab N (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10435 LNCS

Pages Range: 444-452

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC, CAN

ISBN: 9783319661780

DOI: 10.1007/978-3-319-66179-7_51

Abstract

X-ray is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation of the hidden and obscured anatomy in X-ray images remains a challenge and often requires high radiation dose and imaging from several perspectives. In this work, we aim at decomposing the conventional X-ray image into d X-ray components of independent, non-overlapped, clipped sub-volume, that separate rigid structures into distinct layers, leaving all deformable organs in one layer, such that the sum resembles the original input. Our proposed model is validaed on 6 clinical datasets (∼ 7200 X-ray images) in addition to 615 real chest X-ray images. Despite the challenging aspects of modeling such a highly ill-posed problem, exciting and encouraging results are obtained paving the path for further contributions in this direction.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Johns Hopkins University (JHU) US United States (USA) (US)

How to cite

APA:

Albarqouni, S., Fotouhi, J., & Navab, N. (2017). X-ray in-depth decomposition: Revealing the latent structures. In Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 444-452). Quebec City, QC, CAN: Springer Verlag.

MLA:

Albarqouni, Shadi, Javad Fotouhi, and Nassir Navab. "X-ray in-depth decomposition: Revealing the latent structures." Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins, Springer Verlag, 2017. 444-452.

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