Reconstruct Dynamic Soft-Tissue With Stereo Endoscope Based on a Single-Layer Network
In dynamic minimally invasive surgery environments, 3D reconstruction of deformable soft-tissue surfaces with stereo endoscopic images is very challenging. A simple self-supervised stereo reconstruction framework is proposed to address this issue, which bridges the traditional geometric deformable m...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 31(2022) vom: 02., Seite 5828-5840 |
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| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In dynamic minimally invasive surgery environments, 3D reconstruction of deformable soft-tissue surfaces with stereo endoscopic images is very challenging. A simple self-supervised stereo reconstruction framework is proposed to address this issue, which bridges the traditional geometric deformable models and the newly revived neural networks. The equivalence between the classical thin plate spline (TPS) model and a single-layer fully-connected or convolutional network is studied. By alternating training of two TPS equivalent networks within the self-supervised framework, disparity priors are learnt from the past stereo frames of target tissues to form an optimized disparity basis, on which disparity maps of subsequent frames can be estimated more accurately without sacrificing computational efficiency and robustness. The proposed method was verified on stereo-endoscopic videos recorded by the da Vinci® surgical robots |
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| Beschreibung: | Date Completed 12.09.2022 Date Revised 12.09.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2022.3202367 |