https://shura.shu.ac.uk/7180/1/Rodriggues_%2D_optical_techniques_%2D_Review_MIA_revision_v9.pdf

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery.

Intra-operative imaging techniques for obtaining the shape and morphology of soft-tissue surfaces in vivo are a key enabling technology for advanced surgical systems. Different optical techniques for 3-D surface reconstruction in laparoscopy have been proposed, however, so far no quantitative and comparative validation has been performed. Furthermore, robustness of the methods to clinically important factors like smoke or bleeding has not yet been assessed. To address these issues, we have formed a joint international initiative with the aim of validating different state-of-the-art passive and active reconstruction methods in a comparative manner. In this comprehensive in vitro study, we investigated reconstruction accuracy using different organs with various shape and texture and also tested reconstruction robustness with respect to a number of factors like the pose of the endoscope as well as the amount of blood or smoke present in the scene. The study suggests complementary advantages of the different techniques with respect to accuracy, robustness, point density, hardware complexity and computation time. While reconstruction accuracy under ideal conditions was generally high, robustness is a remaining issue to be addressed. Future work should include sensor fusion and in vivo validation studies in a specific clinical context. To trigger further research in surface reconstruction, stereoscopic data of the study will be made publically available at www.open-CAS.com upon publication of the paper.

Comparative validation of single-shot optical techniques for laparoscopic 3-D surface reconstruction.

The emergence of Minimal Access Surgery (MAS) as a paradigm in modern healthcare treatment has created new challenges and opportunities for automated image understanding and computer vision. In MAS, images recovered from inside the body using specialized devices are used to visualize and operate on the surgical site but they can also be used to computationally infer in vivo 3D tissue surface shape, soft-tissue morphology, and surgical instrument motion. This information is important for facilitating in vivo biophotonic imaging modalities where the interaction between light and tissue is used to infer the structural and functional properties of the tissue. This article provides a review of the literature for computer vision and image understanding techniques applied to MAS images. The focus of this article is to elucidate a perspective on how computer vision techniques can be used to support and enhance the capabilities of biophotonic imaging modalities during surgery. Note that while MAS encompasses a variety of surgical specializations this review does not involve procedures performed in the interventional suite. The review has been carried out based on searches in the PubMed and IEEE databases using the article’s keywords.

Surgical Vision

The recent availability of dynamic, dense, and low-cost range imaging has gained widespread interest in health care. It opens up new opportunities and has an increasing impact on both research and commercial activities. This chapter presents a state-of-the-art survey on the integration of modern range imaging sensors into medical applications. The scope is to identify promising applications and methods, and to provide an overview of recent developments in this rapidly evolving domain. The survey covers a broad range of topics, including guidance in computer-assisted interventions, operation room monitoring and workflow analysis, touch-less interaction and on-patient visualization, as well as prevention and support in elderly care and rehabilitation. We put emphasis on dynamic and interactive tasks where real-time and dense 3-D imaging forms the key aspect. While considering different range imaging modalities that fulfill these requirements, we particularly investigate the impact of Time-of-Flight imaging in this domain. Eventually, we discuss practical demands and limitations, and open research issues and challenges that are of fundamental importance for the progression of the field.

/pdf/real-time-range-imaging-in-health-care-a-survey-5bgnniyrmh.pdf

Real-Time Range Imaging in Health Care: A Survey

https://orbilu.uni.lu/bitstream/10993/30242/1/Bernard_shape-aware_surface_reconstruction_orbilu.pdf

Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds

Time-of-Flight (ToF) sensors have become a considerable alternative to conventional surface acquisition techniques such as laser range scanning and stereo vision. Application of ToF cameras for the purpose of intra-operative registration requires matching of the noisy surfaces generated from ToF range data onto pre-interventionally acquired high-resolution surfaces. The contribution of this paper is two-fold: Firstly, we present a novel method for fine rigid registration of noisy ToF data with high-resolution surface meshes taking into account both, the noise characteristics of ToF cameras and the resolution of the target mesh. Secondly, we introduce an evaluation framework for assessing the performance of ToF registration methods based on physically realistic ToF range data generated from a virtual scence. According to experiments within the presented evaluation framework, the proposed method outperforms the standard ICP algorithm with respect to correspondence search and transformation computation, leading to a decrease in the target registration error (TRE) of more than 70%.

/pdf/accounting-for-anisotropic-noise-in-fine-registration-of-pxy0a9ach6.pdf

Accounting for anisotropic noise in fine registration of time-of-flight range data with high-resolution surface data

The Iterative Closest Point (ICP) algorithm is a widely used method for geometric alignment of 3D models. Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point correspondences given the current alignment of the data and computes a rigid transformation accordingly. It can be shown that the method converges to an at least local minimimum with respect to a mean-square distance metric. From a statistical point of view, the algorithm implicitly assumes that the points are observed with isotropic Gaussian noise. In this paper, we (1) present the first variant of the ICP that accounts for anisotropic localization uncertainty in both shapes as well as in both steps of the algorithm and (2) show how to apply the method for robust fine registration of surface meshes. According to an evaluation on medical imaging data, the proposed method is better suited for fine surface registration than the original ICP, reducing the target registration error (TRE) for a set of targets located inside or near the mesh by 80% on average.

Iterative closest point algorithm with anisotropic weighting and its application to fine surface registration

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Accounting for anisotropic noise in fine registration of time-of-flight range data with high-resolution surface data

Iterative closest point algorithm with anisotropic weighting and its application to fine surface registration