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Augmented reality for laparoscopic liver resection is a visualisation mode that allows a surgeon to localise tumours and vessels embedded within the liver by projecting them on top of a laparoscopic image.
Accelerating 3d deep learning with pytorch3d
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Deformation-based augmented reality for hepatic surgery
Haouchine, N., Dequidt, J., Berger, M.O., Cotin, S., 2013 · 2013
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Microsoft COCO: Common objects in context, in: European conference on computer vision, pp. 740–755
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014 · 2014
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Real-time 3d image reconstruction guidance in liver resection surgery
Soler, L., Nicolau, S., Pessaux, P., Mutter, D., Marescaux, J., 2014 · 2014
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A formal method for selecting evaluation metrics for image segmentation, in: 2014 IEEE International Conference on Image Processing (ICIP), pp. 932–936
Taha, A.A., Hanbury, A., del Toro, O.A.J., 2014 · 2014
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al., 2015 · 2015
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Kingma, D., Ba, J., 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Accuracy validation of an image guided laparoscopy system for liver resection, in: Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, SPIE. pp. 52 – 63
Thompson, S., Totz, J., Song, Y., Johnsen, S., Stoyanov, D., Ourselin, S., Gurusamy, K., Schneider, C., Davidson, B., Hawkes, D., Clarkson, M.J., 2015 · 2015
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Using shading to register an intraoperative ct scan to a laparoscopic image, in: Luo, X., Reichl, T., Reiter, A., Mariottini, G.L. (Eds.), Computer-Assisted and Robotic Endoscopy, pp. 59–68
Bernhardt, S., Nicolau, S.A., Bartoli, A., Agnus, V., Soler, L., Doignon, C., 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T.N., Welling, M., 2016 · 2016
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You only look once: Unified, real-time object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788
Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016 · 2016
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EndoNet: A deep architecture for recognition tasks on laparoscopic videos
Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N., 2017 · 2016
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Silhouette-based pose estimation for deformable organs application to surgical augmented reality, in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 539–544
Adagolodjo, Y., Trivisonne, R., Haouchine, N., Cotin, S., Courtecuisse, H., 2017 · 2017
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Rethinking atrous convolution for semantic image segmentation
Chen, L.C., Papandreou, G., Schroff, F., Adam, H., 2017 · 2017
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Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp. 2961–2969
He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017 · 2017
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Deformable registration of a preoperative 3d liver volume to a laparoscopy image using contour and shading cues, in: Medical Image Computing and Computer Assisted Intervention (MICCAI 2017), pp. 326–334
Koo, B., Özgür, E., Le Roy, B., Buc, E., Bartoli, A., 2017 · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J., 2017 · 2017
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Attention U-Net: Learning Where to Look for the Pancreas, in: Medical Imaging with Deep Learning
Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., Glocker, B., Rueckert, D., 2018 · 2018
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Global rigid registration of ct to video in laparoscopic liver surgery
Robu, M.R., Ramalhinho, J., Thompson, S., Gurusamy, K., Davidson, B., Hawkes, D., Stoyanov, D., Clarkson, M.J., 2018 · 2018
Fda: Fourier domain adaptation for semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4085–4095
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Emerging properties in self-supervised vision transformers, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650–9660
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021 · 2021
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nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2021 · 2021
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Balanced chamfer distance as a comprehensive metric for point cloud completion, in: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.. pp. 29088–29100
Wu, T., Pan, L., Zhang, J., WANG, T., Liu, Z., Lin, D., 2021 · 2021
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Cited alongside, same era.
Road extraction by deep residual u-net
Zhang, Z., Liu, Q., Wang, Y., 2018 · 2018
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Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J., 2018 · 2018
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Image-aligned dynamic liver reconstruction using intra-operative field of views for minimal invasive surgery
Cheema, M.N., Nazir, A., Sheng, B., Li, P., Qin, J., Kim, J., Feng, D.D., 2019 · 2019
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Meshcnn: a network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D., 2019 · 2019
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Soft rasterizer: A differentiable renderer for image-based 3d reasoning, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7708–7717
Liu, S., Li, T., Chen, W., Li, H., 2019 · 2019
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An in vivo porcine dataset and evaluation methodology to measure soft-body laparoscopic liver registration accuracy with an extended algorithm that handles collisions
Modrzejewski, R., Collins, T., Seeliger, B., Bartoli, A., Hostettler, A., Marescaux, J., 2019 · 2019
Cited alongside, same era.
Intraoperative liver surface completion with graph convolutional vae, in: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis. Springer, pp. 198–207
Foti, S., Koo, B., Dowrick, T., Ramalhinho, J., Allam, M., Davidson, B., Stoyanov, D., Clarkson, M.J., 2020 · 2020
Cited alongside, same era.
Preoperative to Intraoperative Laparoscopy Fusion Challenge (P2ILF) - MICCAI 2022
Ali, S., Espinel, Y., Jin, Y., Bartoli, A., 2022 · 2022
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Using multiple images and contours for deformable 3d–2d registration of a preoperative ct in laparoscopic liver surgery
Espinel, Y., Calvet, L., Botros, K., Buc, E., Tilmant, C., Bartoli, A., 2022 · 2022
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Automatic, global registration in laparoscopic liver surgery
Koo, B., Robu, M.R., Allam, M., Pfeiffer, M., Thompson, S., Gurusamy, K., Davidson, B., Speidel, S., Hawkes, D., Stoyanov, D., Clarkson, M.J., 2022 · 2022
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Automatic preoperative 3d model registration in laparoscopic liver resection
Labrunie, M., Ribeiro, M., Mourthadhoi, F., Tilmant, C., Le Roy, B., Buc, E., Bartoli, A., 2022 · 2022
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Towards a guideline for evaluation metrics in medical image segmentation
Müller, D., Soto-Rey, I., Kramer, F., 2022 · 2022
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Agisoft Metashape
Agisoft LLC, 2023 · 2023
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The dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science
Carstens, M., Rinner, F.M., Bodenstedt, S., Jenke, A.C., Weitz, J., Distler, M., Speidel, S., Kolbinger, F.R., 2023 · 2023
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Grand Challenge
Radboud University Medical Center, 2023 · 2023
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Unity Asset Store
Unity Technologies, 2023 · 2023
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Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark
Wagner, M., Müller-Stich, B.P., Kisilenko, A., Tran, D., Heger, P., Mündermann, L., Lubotsky, D.M., Müller, B., Davitashvili, T., Capek, M., Reinke, A., Reid, C., Yu, T., Vardazaryan, A., Nwoye, C.I., Padoy, N., Liu, X., Lee, E.J., Disch, C., Meine, H., Xia, T., Jia, F., Kondo, S., Reiter, W., Jin, Y., Long, Y., Jiang, M., Dou, Q., Heng, P.A., Twick, I., Kirtac, K., Hosgor, E., Bolmgren, J.L., Stenzel, M., von Siemens, B., Zhao, L., Ge, Z., Sun, H., Xie, D., Guo, M., Liu, D., Kenngott, H.G., Nickel, F., von Frankenberg, M., Mathis-Ullrich, F., Kopp-Schneider, A., Maier-Hein, L., Speidel, S., Bodenstedt, S., 2023 · 2023
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