Occlusion-robust scene flow-based tissue deformation recovery incorporating a mesh optimization model

• Published in: International journal for computer assisted radiology and surgery 2023-06, Vol.18 (6), p.1043-1051
• Main Authors: Chen, Jiahe, Hara, Kazuaki, Kobayashi, Etsuko, Sakuma, Ichiro, Tomii, Naoki
• Format: Article
• Language: English
• Subjects: Algorithms; Binocular vision; Biomechanics; Computer Imaging; Computer Science; Finite element method; Health Informatics; Humans; Imaging; In vivo methods and tests; Medical instruments; Medicine; Medicine & Public Health; Minimally Invasive Surgical Procedures - methods; Modules; Motion; Occlusion; Optimization models; Original; Original Article; Pattern Recognition and Graphics; Radiology; Recovery; Robustness; Surgery; Surgery, Computer-Assisted - methods; Surgical instruments; Surgical Mesh; Three dimensional models; Three dimensional motion; Vision
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-023-02889-z

Purpose Tissue deformation recovery is to reconstruct the change in shape and surface strain caused by tool-tissue interaction or respiration, which is essential for providing motion and shape information that benefits the improvement of the safety of minimally invasive surgery. The binocular vision-based approach is a practical candidate for deformation recovery as no extra devices are required. However, previous methods suffer from limitations such as the reliance on biomechanical priors and the vulnerability to the occlusion caused by surgical instruments. To address the issues, we propose a deformation recovery method incorporating mesh structures and scene flow. Methods The method can be divided into three modules. The first one is the implementation of the two-step scene flow generation module to extract the 3D motion from the binocular sequence. Second, we propose a strain-based filtering method to denoise the original scene flow. Third, a mesh optimization model is proposed that strengthens the robustness to occlusion by employing contextual connectivity. Results In a phantom and an in vivo experiment, the feasibility of the method in recovering surface deformation in the presence of tool-induced occlusion was demonstrated. Surface reconstruction accuracy was quantitatively evaluated by comparing the recovered mesh surface with the 3D scanned model in the phantom experiment. Results show that the overall error is 0.70 ± 0.55 mm. Conclusion The method has been demonstrated to be capable of continuously recovering surface deformation using mesh representation with robustness to the occlusion caused by surgical forceps and promises to be suitable for the application in actual surgery.