Superficial vessel reconstruction with a Multiview camera system

Marreiros, Filipe Miguel Maria; Rossitti, Sandro; Karlsson, Per M.; Wang, Chunliang; Gustafsson, Torbjörn; Carleberg, Per

doi:10.1117/1.jmi.3.1.019801

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…In [8] brain displacements are estimated by manually defining vessel centerlines and then using them to drive a Coherent Point Drift matching algorithm to register preoperative MRI scans. This method was later improved to extract vessel centerlines from multiview cameras and reconstruct their 3D shape [9]. Using an optically tracked stylus Luo et al [10] manually identified cortical vessel features to estimate brain shift and corrected it for after dural opening using a model-based registration.…”

Section: Introductionmentioning

confidence: 99%

Cortical Vessel Segmentation for Neuronavigation Using Vesselness-Enforced Deep Neural Networks

Haouchine

Nercessian

Juvekar

et al. 2022

IEEE Trans. Med. Robot. Bionics

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We propose in this paper an efficient method to segment cortical vessels in craniotomy images acquired by the surgical microscope. Our method uses a vesselness-enforced convolutional neural network to classify each pixel of a craniotomy image as a vessel or surrounding tissue. This permits training the network not only on appearance-based features but also on geometrical-based constraints that will ensure the continuity of the vascular trees. Our solution uses neural style transfer to generate new instances of images from manually labeled data leading to augment the training dataset in an anatomically semantic manner. The generated images improve the generalization of our model to various types of cortical surface appearances and vascular geometries. We conducted experiments on real images from human patients that demonstrate that accurate intraoperative cortical vessel segmentation can be achieved.

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Section: Introductionmentioning

confidence: 99%

Cortical Vessel Segmentation for Neuronavigation Using Vesselness-Enforced Deep Neural Networks

Haouchine

Nercessian

Juvekar

et al. 2022

IEEE Trans. Med. Robot. Bionics

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“…Stereo camera systems represent an effective instrument for 3D reconstruction in great variety of applications and in different fields of science, entertainment, industry and automotive, [1], [2], [3], [4], [5], [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Stereo camera system calibration: the need of two sets of parameters

Beschi,

Feng,

Melillo

et al. 2021

Preprint

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The reconstruction of a scene via a stereo-camera system is a two-steps process, where at first images from different cameras are matched to identify the set of point-to-point correspondences that then will actually be reconstructed in the three dimensional real world. The performance of the system strongly relies of the calibration procedure, which has to be carefully designed to guarantee optimal results. We implemented three different calibration methods and we compared their performance over 19 datasets. We present the experimental evidence that, due to the image noise, a single set of parameters is not sufficient to achieve high accuracy in the identification of the correspondences and in the 3D reconstruction at the same time. We propose to calibrate the system twice to estimate two different sets of parameters: the one obtained by minimizing the reprojection error that will be used when dealing with quantities defined in the 2D space of the cameras, and the one obtained by minimizing the reconstruction error that will be used when dealing with quantities defined in the real 3D world.

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“…I N recent years technological advances in the field of imaging and computer vision, together with the growing demand for 3D contents, contributed to make digital camera stereo systems accurate in the 3D reconstruction and at the same time accessible to a wide audience. This led to the proliferation of stereo vision applications in fields as diverse as entertainment [1]- [3], surveillance [4]- [7], navigation [8]- [11], robotics [12]- [15], medicine [16]- [19] and biology [20]- [23].…”

Section: Introductionmentioning

confidence: 99%

CoMo: A novel co-moving 3D camera system

Cavagna¹,

Xing²,

Melillo³

et al. 2021

Preprint

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Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a co-moving camera system of two synchronized high speed cameras coupled with rotational stages, which allow us to dynamically follow the motion of a target flock. With the rotation of the cameras we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but at the same time we change in time the external parameters of the system, which have then to be calibrated frame-by-frame. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system home configuration (rotational stage at an angle equal to 0 • ) and combining this static information with the time dependent rotation due to the stages. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%. The novelty of the work presented in this paper is not only on the system itself, but also on the approach we use in the tests, which we show to be a very powerful tool in detecting and fixing calibration inaccuracies and that, for this reason, may be relevant for a broad audience.

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Superficial vessel reconstruction with a Multiview camera system

Cited by 4 publications

References 20 publications

Cortical Vessel Segmentation for Neuronavigation Using Vesselness-Enforced Deep Neural Networks

Cortical Vessel Segmentation for Neuronavigation Using Vesselness-Enforced Deep Neural Networks

Stereo camera system calibration: the need of two sets of parameters

CoMo: A novel co-moving 3D camera system

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