Page 52 - Read Online
P. 52
Page 136 Ding et al. Art Int Surg 2024;4:109-38 https://dx.doi.org/10.20517/ais.2024.16
219. Hwang SJ, Park SJ, Kim GM, Baek JH. Unsupervised monocular depth estimation for colonoscope system using feedback network.
Sensors 2021;21:2691. DOI PubMed PMC
220. Li W, Hayashi Y, Oda M, Kitasaka T, Misawa K, Mori K. Geometric constraints for self-supervised monocular depth estimation on
laparoscopic images with dual-task consistency. In: Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors. Medical Image Computing
and Computer Assisted Intervention - MICCAI 2022. Cham: Springer; 2022. pp. 467-77. DOI
221. Masuda T, Sagawa R, Furukawa R, Kawasaki H. Scale-preserving shape reconstruction from monocular endoscope image sequences
by supervised depth learning. Healthc Technol Lett 2024;11:76-84. DOI PubMed PMC
222. Tukra S, Giannarou S. Randomly connected neural networks for self-supervised monocular depth estimation. Comput Methods
Biomech Biomed Eng Imaging Vis 2022;10:390-9. DOI
223. Zhao S, Wang C, Wang Q, Liu Y, Zhou SK. 3D endoscopic depth estimation using 3d surface-aware constraints. arXiv. [Preprint.]
Mar 4, 2022 [accessed 2024 Jul 3]. Available from: https://arxiv.org/abs/2203.02131.
224. Han J, Jiang Z, Feng G. Monocular depth estimation based on chained residual pooling and gradient weighted loss. In: 2023 3rd
International Conference on Consumer Electronics and Computer Engineering (ICCECE); 2023 Jan 6-8; Guangzhou, China. IEEE;
2023. pp. 278-82. DOI
225. Yang Y, Shao S, Yang T, et al. A geometry-aware deep network for depth estimation in monocular endoscopy. Eng Appl Artif Intell
2023;122:105989. DOI
226. Zhang G, Gao X, Meng H, Pang Y, Nie X. A self-supervised network-based smoke removal and depth estimation for monocular
endoscopic videos. IEEE Trans Vis Comput Graph 2024;30:6547-59. DOI PubMed
227. Yang L, Kang B, Huang Z, Xu X, Feng J, Zhao H. Depth anything: unleashing the power of large-scale unlabeled data. arXiv.
[Preprint.] Apr 7, 2024 [accessed 2024 Jul 3]. Available from: https://arxiv.org/abs/2401.10891.
228. Han JJ, Acar A, Henry C, Wu JY. Depth anything in medical images: a comparative study. arXiv. [Preprint.] Jan 29, 2024 [accessed
2024 Jul 3]. Available from: https://arxiv.org/abs/2401.16600.
229. Chang JR, Chen YS. Pyramid stereo matching network. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern
Recognition; 2018 Jun 18-23; Salt Lake City, UT, USA. IEEE; 2018. pp. 5410-8. DOI
230. Luo W, Schwing AG, Urtasun R. Efficient deep learning for stereo matching. In: 2016 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, NV, USA. IEEE; 2016. pp. 5695-703. DOI
231. Zampokas G, Peleka G, Tsiolis K, Topalidou-Kyniazopoulou A, Mariolis I, Tzovaras D. Real-time stereo reconstruction of
intraoperative scene and registration to preoperative 3D models for augmenting surgeons’ view during RAMIS. Med Phys
2022;49:6517-26. DOI PubMed
232. Probst T, Maninis K, Chhatkuli A, Ourak M, Poorten EV, Van Gool L. Automatic tool landmark detection for stereo vision in robot-
assisted retinal surgery. IEEE Robot Autom Lett 2018;3:612-9. DOI
233. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv.
[Preprint.] Jun 3, 2021 [accessed 2024 Jul 3]. Available from: https://arxiv.org/abs/2010.11929.
234. Tao R, Huang B, Zou X, Zheng G. SVT-SDE: spatiotemporal vision transformers-based self-supervised depth estimation in
stereoscopic surgical videos. IEEE Trans Med Robot Bionics 2023;5:42-53. DOI
235. Li Z, Liu X, Drenkow N, et al. Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers. In:
2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021 Oct 10-17; Montreal, QC, Canada. IEEE; 2021. pp.
6177-86. DOI
236. Long Y, Li Z, Yee CH, et al. E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth
perception. In: de Bruijne M, et al., editors. Medical Image Computing and Computer Assisted Intervention - MICCAI 2021. Cham:
Springer; 2021. pp. 415-25. DOI
237. Guo W, Li Z, Yang Y, et al. Context-enhanced stereo transformer. In: Avidan S, Brostow G, Cissé M, Farinella GM, Hassner T,
editors. Computer Vision - ECCV 2022. Cham: Springer; 2022. pp. 263-79. DOI
238. Hu X, Baena FRY. Automatic bone surface restoration for markerless computer-assisted orthopaedic surgery. Chin J Mech Eng
2022;35:18. DOI
239. Wang W, Zhou H, Yan Y, et al. An automatic extraction method on medical feature points based on PointNet++ for robot-assisted
knee arthroplasty. Int J Med Robot 2023;19:e2464. DOI PubMed
240. Baum ZMC, Hu Y, Barratt DC. Multimodality biomedical image registration using free point transformer networks. In: Hu Y, et al.,
editors. Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. Cham: Springer; 2020. pp. 116-25. DOI
241. Baum ZMC, Hu Y, Barratt DC. Real-time multimodal image registration with partial intraoperative point-set data. Med Image Anal
2021;74:102231. DOI PubMed PMC
242. Widya AR, Monno Y, Imahori K, et al. 3D reconstruction of whole stomach from endoscope video using structure-from-motion.
Annu Int Conf IEEE Eng Med Biol Soc 2019;2019:3900-4. DOI
243. Lin B, Sun Y, Qian X, Goldgof D, Gitlin R, You Y. Video-based 3D reconstruction, laparoscope localization and deformation
recovery for abdominal minimally invasive surgery: a survey. Int J Med Robot 2016;12:158-78. DOI PubMed
244. Song J, Wang J, Zhao L, Huang S, Dissanayake G. Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal
invasive surgery. IEEE Robot Autom Lett 2018;3:155-62. DOI
245. Zhou H, Jagadeesan J. Real-time dense reconstruction of tissue surface from stereo optical video. IEEE Trans Med Imaging
2020;39:400-12. DOI PubMed PMC

