Multicamera Laparoscopic Imaging With Tunable Focusing Capability

Kanhere, Aditi; Grinsven, Kari L. Van; Huang, Chi-Chieh; Lu, Yi; Greenberg, Jacob A.; Heise, Charles P.; Hu, Yu; Jiang, Hongrui

doi:10.1109/jmems.2014.2360124

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…Camera arrays have extensive applications in surveillance [ 1 ], robotics [ 2 ], Virtual Reality [ 3 , 4 , 5 ], surgery [ 6 ], and more. Multiple cameras placed on proper locations and poses may offer a wider field of view (FOV) by aggregating (stitching) individual images into a coherent, extended mosaic beyond what a single camera can provide.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Watras

Kim

Liu

et al. 2018

Sensors

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An optimal camera placement problem is investigated. The objective is to maximize the area of the field of view (FoV) of a stitched video obtained by stitching video streams from an array of cameras. The positions and poses of these cameras are restricted to a given set of selections. The camera array is designed to be placed inside the abdomen to support minimally invasive laparoscopic surgery. Hence, a few non-traditional requirements/constraints are imposed: Adjacent views are required to overlap to support image registration for seamless video stitching. The resulting effective FoV should be a contiguous region without any holes and should be a convex polygon. With these requirements, traditional camera placement algorithms cannot be directly applied to solve this problem. In this work, we show the complexity of this problem grows exponentially as a function of the problem size, and then present a greedy polynomial time heuristic solution that approximates well to the globally optimal solution. We present a new approach to directly evaluate the combined coverage area (area of FoV) as the union of a set of quadrilaterals. We also propose a graph-based approach to ensure the stitching requirement (overlap between adjacent views) is satisfied. We present a method to find a convex polygon with maximum area from a given polygon. Several design examples show that the proposed algorithm can achieve larger FoV area while using much less computing time.

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Section: Introductionmentioning

confidence: 99%

“…The camera placement problem is formulated in the context of building a camera array to enhance the vision quality of a laparoscopic surgery [ 6 ]. In current laparoscopic surgery, a single-camera with lighting is inserted through a trocar to provide desired view for surgeons to carry out surgery.…”

Section: Introductionmentioning

confidence: 99%

Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Watras

Kim

Liu

et al. 2018

Sensors

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“…Wang et al [20] demonstrated a 3D integral-imaging endoscopy with tunable DOF by using a single large-aperture focal-length-tunable liquid crystal (LC) lens. Kanhere et al [21] introduced a multi-camera system to capture different views and stitch them together for a wider view. The above-mentioned methods add complexity to the laparoscope.…”

Section: Introductionmentioning

confidence: 99%

A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses

Mao,

Huang,

et al. 2024

IEEE J. Transl. Eng. Health Med.

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During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.

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“…The miniaturized cameras are easily deployed and retrieved using the mechanical system of the TCA. Such convenient deployment and retrieval of the miniature cameras offered by this trocar design is an advantage over our previous attempts to apply multiple cameras at laparoscopic ports [ 22 , 23 , 24 ]. The TCA can provide a larger FoV compared to a current laparoscopic imaging system that uses a single camera, with the use of multiple miniaturized cameras.…”

Section: Introductionmentioning

confidence: 99%

Large-Field-of-View Visualization Utilizing Multiple Miniaturized Cameras for Laparoscopic Surgery

et al. 2018

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The quality and the extent of intra-abdominal visualization are critical to a laparoscopic procedure. Currently, a single laparoscope is inserted into one of the laparoscopic ports to provide intra-abdominal visualization. The extent of this field of view (FoV) is rather restricted and may limit efficiency and the range of operations. Here we report a trocar-camera assembly (TCA) that promises a large FoV, and improved efficiency and range of operations. A video stitching program processes video data from multiple miniature cameras and combines these videos in real-time. This stitched video is then displayed on an operating monitor with a much larger FoV than that of a single camera. In addition, we successfully performed a standard and a modified bean drop task, without any distortion, in a simulator box by using the TCA and taking advantage of its FoV which is larger than that of the current laparoscopic cameras. We successfully demonstrated its improved efficiency and range of operations. The TCA frees up a surgical port and potentially eliminates the need of physical maneuvering of the laparoscopic camera, operated by an assistant.

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Multicamera Laparoscopic Imaging With Tunable Focusing Capability

Cited by 10 publications

References 20 publications

Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

Optimal Camera Pose and Placement Configuration for Maximum Field-of-View Video Stitching

A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses

Large-Field-of-View Visualization Utilizing Multiple Miniaturized Cameras for Laparoscopic Surgery

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