Koo B, Robu MR, Allam M, Pfeiffer M, Thompson S, Gurusamy K, Davidson B, Speidel S, Hawkes D, Stoyanov D, Clarkson MC (2022) Automatic, global registration in laparoscopic liver surgery. International Journal of Computer Assisted Radiology and Surgery 1–10
Hampshire J, Dicken BJ, Uruththirakodeeswaran T, Punithakumar K, Noga M (2023) Pediatric patient-specific three-dimensional virtual models for surgical decision making in resection of hepatic and retroperitoneal tumors. Int J Comput Assist Radiol Surg 18(10):1941–1949
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. MICCAI 424–432 Springer
Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D (2022) Unetr: Transformers for 3d medical image segmentation. WACV 574–584
Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) Doubleu-net: A deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-based Medical Systems (CBMS), 558–564. IEEE
Dai W, Dong N, Wang Z, Liang X, Zhang H, Xing EP (2018) Scan: Structure correcting adversarial network for organ segmentation in chest x-rays. In: DLMIA 2018, pp. 263–273 (2018). Springer
Araújo RJ, Cardoso JS, Oliveira HP (2019) A deep learning design for improving topology coherence in blood vessel segmentation. In: MICCAI, pp. 93–101. Springer
Marimont SN, Tarroni G (2022) Implicit u-net for volumetric medical image segmentation. In: Annual Conference on Medical Image Understanding and Analysis, pp. 387–397. Springer
Sørensen K, Camara O, De Backer O, Kofoed KF, Paulsen RR (2022) Nudf: Neural unsigned distance fields for high resolution 3d medical image segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE
Khan MO, Fang Y (2022) Implicit neural representations for medical imaging segmentation. In: MICCAI, pp. 433–443. Springer
Amiranashvili T, Lüdke D, Li HB, Zachow S, Menze BH (2024) Learning continuous shape priors from sparse data with neural implicit functions. Medical Image Analysis, 103099
Park JJ, Florence P, Straub J, Newcombe R, Lovegrove S (2019) Deepsdf: Learning continuous signed distance functions for shape representation. In: CVPR, pp. 165–174
Sitzmann V, Martel J, Bergman A, Lindell D, Wetzstein G (2020) Implicit neural representations with periodic activation functions. Adv Neural Inf Process Syst 33:7462–7473
David H, Andrew MD, Quoc VL (2017) Hypernetworks. In: ICLR
Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM (2022) The Medical segmentation decathlon Nature Commun 13(1):4128
Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM (2015) eeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: MICCAI, pp. 556–564. Springer
Tang H, Zhang C, Xie X (2019) Automatic pulmonary lobe segmentation using deep learning. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1225–1228. IEEE
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