3D semantic mapping from arthroscopy using out-of-distribution pose and depth and in-distribution segmentation training
Date
2021
Authors
Jonmohamadi, Y.
Ali, S.
Liu, F.
Roberts, J.
Crawford, R.
Carneiro, G.
Pandey, A.K.
Editors
DeBruijne, M.
Cattin, P.C.
Cotin, S.
Padoy, N.
Speidel, S.
Zheng, Y.
Essert, C.
Cattin, P.C.
Cotin, S.
Padoy, N.
Speidel, S.
Zheng, Y.
Essert, C.
Advisors
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Conference paper
Citation
Lecture Notes in Artificial Intelligence, 2021 / DeBruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (ed./s), vol.12902 LNCS, pp.383-393
Statement of Responsibility
Yaqub Jonmohamadi, Shahnewaz Ali, Fengbei Liu, Jonathan Roberts, Ross Crawford, Gustavo Carneiro, Ajay K. Pandey
Conference Name
24th International Conference of Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (27 Sep 2021 - 1 Oct 2021 : Strasbourg, France)
Abstract
Minimally invasive surgery (MIS) has many documented advantages, but the surgeon’s limited visual contact with the scene can be problematic. Hence, systems that can help surgeons navigate, such as a method that can produce a 3D semantic map, can compensate for the limitation above. In theory, we can borrow 3D semantic mapping techniques developed for robotics, but this requires finding solutions to the following challenges in MIS: 1) semantic segmentation, 2) depth estimation, and 3) pose estimation. In this paper, we propose the first 3D semantic mapping system from knee arthroscopy that solves the three challenges above. Using out-of-distribution non-human datasets, where pose could be labeled, we jointly train depth+pose estimators using self-supervised and supervised losses. Using an in-distribution human knee dataset, we train a fully-supervised semantic segmentation system to label arthroscopic image pixels into femur, ACL, and meniscus. Taking testing images from human knees, we combine the results from these two systems to automatically create 3D semantic maps of the human knee. The result of this work opens the pathway to the generation of intra-operative 3D semantic mapping, registration with pre-operative data, and robotic-assisted arthroscopy. Source code: https://github.com/YJonmo/EndoMapNet.
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© Springer Nature Switzerland AG 2021