eprintid: 1566574 rev_number: 28 eprint_status: archive userid: 608 dir: disk0/01/56/65/74 datestamp: 2017-07-23 09:46:48 lastmod: 2021-09-23 22:24:27 status_changed: 2017-11-14 16:58:21 type: proceedings_section metadata_visibility: show creators_name: Hu, Y creators_name: Gibson, E creators_name: Lee, LL creators_name: Xie, W creators_name: Barratt, DC creators_name: Vercauteren, T creators_name: Noble, JA title: Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F42 note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: Sonography synthesis has a wide range of applications, including medical procedure simulation, clinical training and multimodality image registration. In this paper, we propose a machine learning approach to simulate ultrasound images at given 3D spatial locations (relative to the patient anatomy), based on conditional generative adversarial networks (GANs). In particular, we introduce a novel neural network architecture that can sample anatomically accurate images conditionally on spatial position of the (real or mock) freehand ultrasound probe. To ensure an effective and efficient spatial information assimilation, the proposed spatially-conditioned GANs take calibrated pixel coordinates in global physical space as conditioning input, and utilise residual network units and shortcuts of conditioning data in the GANs’ discriminator and generator, respectively. Using optically tracked B-mode ultrasound images, acquired by an experienced sonographer on a fetus phantom, we demonstrate the feasibility of the proposed method by two sets of quantitative results: distances were calculated between corresponding anatomical landmarks identified in the held-out ultrasound images and the simulated data at the same locations unseen to the networks; a usability study was carried out to distinguish the simulated data from the real images. In summary, we present what we believe are state-of-the-art visually realistic ultrasound images, simulated by the proposed GAN architecture that is stable to train and capable of generating plausibly diverse image samples. date: 2017-09-09 date_type: published publisher: Springer official_url: http://dx.doi.org/10.1007/978-3-319-67564-0_11 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1405459 doi: 10.1007/978-3-319-67564-0_11 isbn_13: 9783319675633 lyricists_name: Barratt, Dean lyricists_name: Gibson, Eli lyricists_name: Hu, Yipeng lyricists_name: Vercauteren, Tom lyricists_id: DBARR55 lyricists_id: EGIBS90 lyricists_id: YHUXX66 lyricists_id: TVERC65 actors_name: Allington-Smith, Dominic actors_id: DAALL44 actors_role: owner full_text_status: public series: Lecture Notes in Computer Science publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) volume: 10555 place_of_pub: Cham, Switzerland pagerange: 105-115 event_title: Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, Held in Conjunction with MICCAI 2017 issn: 1611-3349 book_title: Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings editors_name: Cardoso, MJ editors_name: Arbel, T editors_name: Gao, F editors_name: Kainz, B editors_name: Van Walsum, T editors_name: Shi, K editors_name: Bhatia, KK editors_name: Peter, R editors_name: Vercauteren, T editors_name: Reyes, M editors_name: Dalca, A editors_name: Wiest, R editors_name: Niessen, W editors_name: Emmer, BJ citation: Hu, Y; Gibson, E; Lee, LL; Xie, W; Barratt, DC; Vercauteren, T; Noble, JA; (2017) Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks. In: Cardoso, MJ and Arbel, T and Gao, F and Kainz, B and Van Walsum, T and Shi, K and Bhatia, KK and Peter, R and Vercauteren, T and Reyes, M and Dalca, A and Wiest, R and Niessen, W and Emmer, BJ, (eds.) Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings. (pp. pp. 105-115). Springer: Cham, Switzerland. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/1566574/1/Vercauteren_1707.05392v1.pdf