Online Calibration of Extrinsic Parameters for Solid-State LIDAR Systems.

This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems-as required for calibration-is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 cm was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring.

Automatic cochlear multimodal 3D image segmentation and analysis using atlas-model-based method.

Objectives: To propose an automated fast cochlear segmentation, length, and volume estimation method from clinical 3D multimodal images which has a potential role in the choice of cochlear implant type, surgery planning, and robotic surgeries.Methods: Two datasets from different countries were used. These datasets include 219 clinical 3D images of cochlea from 3 modalities: CT, CBCT, and MR. The datasets include different ages, genders, and types of cochlear implants. We propose an atlas-model-based method for cochlear segmentation and measurement based on high-resolution µCT model and A-value. The method was evaluated using 3D landmarks located by two experts.Results: The average error was 0.61±0.22 mm and the average time required to process an image was 5.21±0.93 seconds (P<0.001). The volume of the cochlea ranged from 73.96 mm3 to 106.97 mm3, the cochlear length ranged from 36.69 to 45.91 mm at the lateral wall and from 29.12 to 39.05 mm at the organ of Corti.Discussion: We propose a method that produces nine different automated measurements of the cochlea: volume of scala tympani, volume of scala vestibuli, central lengths of the two scalae, the scala tympani lateral wall length, and the organ of Corti length in addition to three measurements related to A-value.Conclusion: This automatic cochlear image segmentation and analysis method can help clinician process multimodal cochlear images in approximately 5 seconds using a simple computer. The proposed method is publicly available for free download as an extension for 3D Slicer software.

On the reproducibility of extrusion-based bioprinting: round robin study on standardization in the field.

The outcome of three-dimensional (3D) bioprinting heavily depends, amongst others, on the interaction between the developed bioink, the printing process, and the printing equipment. However, if this interplay is ensured, bioprinting promises unmatched possibilities in the health care area. To pave the way for comparing newly developed biomaterials, clinical studies, and medical applications (i.e. printed organs, patient-specific tissues), there is a great need for standardization of manufacturing methods in order to enable technology transfers. Despite the importance of such standardization, there is currently a tremendous lack of empirical data that examines the reproducibility and robustness of production in more than one location at a time. In this work, we present data derived from a round robin test for extrusion-based 3D printing performance comprising 12 different academic laboratories throughout Germany and analyze the respective prints using automated image analysis (IA) in three independent academic groups. The fabrication of objects from polymer solutions was standardized as much as currently possible to allow studying the comparability of results from different laboratories. This study has led to the conclusion that current standardization conditions still leave room for the intervention of operators due to missing automation of the equipment. This affects significantly the reproducibility and comparability of bioprinting experiments in multiple laboratories. Nevertheless, automated IA proved to be a suitable methodology for quality assurance as three independently developed workflows achieved similar results. Moreover, the extracted data describing geometric features showed how the function of printers affects the quality of the printed object. A significant step toward standardization of the process was made as an infrastructure for distribution of material and methods, as well as for data transfer and storage was successfully established.

Automatic intra-subject registration and fusion of multimodal cochlea 3D clinical images.

BACKGROUND: The postoperative imaging assessment of Cochlear Implant (CI) patients is imperative. The main obstacle is that Magnetic Resonance imaging (MR) is contraindicated or hindered by significant artefacts in most cases with CIs. This study describes an automatic cochlear image registration and fusion method that aims to help radiologists and surgeons to process pre-and postoperative 3D multimodal imaging studies in cochlear implant (CI) patients. METHODS AND FINDINGS: We propose a new registration method, Automatic Cochlea Image Registration (ACIR-v3), which uses a stochastic quasi-Newton optimiser with a mutual information metric to find 3D rigid transform parameters for registration of preoperative and postoperative CI imaging. The method was tested against a clinical cochlear imaging dataset that contains 131 multimodal 3D imaging studies of 41 CI patients with preoperative and postoperative images. The preoperative images were MR, Multidetector Computed Tomography (MDCT) or Cone Beam Computed Tomography (CBCT) while the postoperative were CBCT. The average root mean squared error of ACIR-v3 method was 0.41 mm with a standard deviation of 0.39 mm. The results were evaluated quantitatively using the mean squared error of two 3D landmarks located manually by two neuroradiology experts in each image and compared to other previously known registration methods, e.g. Fast Preconditioner Stochastic Gradient Descent, in terms of accuracy and speed. CONCLUSIONS: Our method, ACIR-v3, produces high resolution images in the postoperative stage and allows for visualisation of the accurate anatomical details of the MRI with the absence of significant metallic artefacts. The method is implemented as an open-source plugin for 3D Slicer tool.

A quadratic programming approach for the mosaicing of virtual slides that incorporates the positioning accuracy of the microscope stage.

We describe a novel approach for creating virtual slides that incorporates the positioning accuracy of the microscope stage in the optimization step. To capture a complete slide in microscopy, a large number of fields of view have to be acquired by moving the microscope stage in a controlled way. These fields of view are aligned in such a way that a globally consistent virtual slide is formed. However, depending on the positioning repeatability of the stage and the accuracy of the stage calibration, this results in alignment errors. These errors are usually resolved by applying a mosaicing algorithm. Our algorithm extends known mosaicing approaches by analyzing the positioning accuracy of the stage and incorporating this knowledge to make the mosaicing process more robust.

A spectral color correction framework for medical applications.

This paper presents a new spectral approach to color correction for medical image analysis applications. Linear estimation with regularization by a constrained principal eigenvector method is used for calibration of the camera system and estimation of the illumination spectrum while spectral surface reflectivities are determined by Wiener inverse estimation. Nonlinear devices are handled by piecewise linear interpolation and any linear color preprocessing inside the camera is explicitly modeled. All measurement and estimation processes are combined into a spectral calibration framework for practical application in computer-assisted image analysis. The novelty of our approach lies in the generalization of the image formation model allowing for linear preprocessing inside the camera system. Such transforms would lead to erroneous results with positivity constraint based algorithms or a monochromator based measurement. We provide experimental results from a comprehensive set of reference measurements acquired with a video endoscopy system for gastroscopic application.