TY - GEN
T1 - Novel View Synthesis for Surgical Recording
AU - Masuda, Mana
AU - Saito, Hideo
AU - Takatsume, Yoshifumi
AU - Kajita, Hiroki
N1 - Funding Information:
Acknowledgement. We would like to express our gratitude to Yusuke Sekikawa, Denso IT Laboratory, Japan. Without his kind advice, this work would not have been completed. We also would like to thank the reviewers for their valuable comment. This work was supported by MHLW Health, Labour, and Welfare Sciences Research Grants Research on Medical ICT and Artificial Intelligence Program Grant Number 20AC1004, the MIC/SCOPE 201603003, and JSPS KAKENHI Grant Number 22H03617.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Recording surgery in operating rooms is one of the essential tasks for education and evaluation of medical treatment. However, recording the fields which depict the surgery is difficult because the targets are heavily occluded during surgery by the heads or hands of doctors or nurses. We use a recording system which multiple cameras embedded in the surgical lamp, assuming that at least one camera is recording the target without occlusion. In this paper, we propose Conditional-BARF (C-BARF) to generate occlusion-free images by synthesizing novel view images from the camera, aiming to generate videos with smooth camera pose transitions. To the best of our knowledge, this is the first work to tackle the problem of synthesizing a novel view image from multiple images for the surgery scene. We conduct experiments using an original dataset of three different types of surgeries. Our experiments show that we can successfully synthesize novel views from the images recorded by the multiple cameras embedded in the surgical lamp.
AB - Recording surgery in operating rooms is one of the essential tasks for education and evaluation of medical treatment. However, recording the fields which depict the surgery is difficult because the targets are heavily occluded during surgery by the heads or hands of doctors or nurses. We use a recording system which multiple cameras embedded in the surgical lamp, assuming that at least one camera is recording the target without occlusion. In this paper, we propose Conditional-BARF (C-BARF) to generate occlusion-free images by synthesizing novel view images from the camera, aiming to generate videos with smooth camera pose transitions. To the best of our knowledge, this is the first work to tackle the problem of synthesizing a novel view image from multiple images for the surgery scene. We conduct experiments using an original dataset of three different types of surgeries. Our experiments show that we can successfully synthesize novel views from the images recorded by the multiple cameras embedded in the surgical lamp.
KW - Generative model
KW - Novel view synthesis
KW - Surgery recording
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U2 - 10.1007/978-3-031-18576-2_7
DO - 10.1007/978-3-031-18576-2_7
M3 - Conference contribution
AN - SCOPUS:85141813384
SN - 9783031185755
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 67
EP - 76
BT - Deep Generative Models - 2nd MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Mukhopadhyay, Anirban
A2 - Oksuz, Ilkay
A2 - Engelhardt, Sandy
A2 - Zhu, Dajiang
A2 - Yuan, Yixuan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention, DGM4MICCAI 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -