This protocol describes novoSpaRc, a computational pipeline for de novo reconstruction of spatial gene expression from single-cell RNA sequencing with the potential to incorporate spatial atlas data to improve the reconstruction.

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this paper, real-time needle tip tracking method using 2D ultrasound (US) images for robotic biopsies is presented. In this method, the needle tip is estimated with the Gabor filter based image processing algorithm, and the estimation noise is reduced with the Kalman filter. This paper also presents the needle tip tracking simulation to test accuracy of the Kalman filter under position misalignments and tissue deformations. In order to execute proposed method in real-time, the bin packing method is used and the processing time is reduced by 56%, without a GPU. The proposed method was tested in four different phantoms and water medium. The accuracy of the needle tip estimation was measured with optical tracking system, and root mean square error (RMS) of the tip position is found to be 1.17 mm. The experiments showed that the algorithm could track the needle tip in real-time.TÜBİTA

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Ultrasound (US) is one the most commonly used medical imaging techniques in percutaneous needle procedures. However, US images are inherently noisy and contain excessive number of artifacts. Hence, it is not easy to track the needle tip in the US images during the needle insertions. At this point, image based visual tracking techniques can be used for needle tip tracking. This paper presents a method for visual tracking of biopsy needles in 2D US images using sum of squared differences and sum of conditional variances. Second order Gauss-Newton optimization is used to decrease processing time and make the tracking more robust. The needle template images used in the method are updated with a strategy to prevent needle loss and detection failures during tracking. The paper also explains how to identify needle losses during tracking and how to recover the needle position without using a needle localization algorithm. We demonstrate the precision of the visual needle tip tracking method with experiments under challenging tracking conditions.TÜBİTA

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Percutaneous needle procedures are mostly carried out with the guidance of 2D ultrasound (US) imaging. US images are inherently noisy and their resolutions are low. Hence, target tracking can be challenging. Image based tracking methods can be used to track the needle and the target. This paper proposes visual tracking of multiple moving points, such as biopsy needles and targets, in 2D US images using normalized cross correlation and mutual information similarity functions. Both moving and deformable targets can be tracked. An affine motion model is used for small and moving target tracking and a thin plate spline motion model is used for deformable target tracking. During the tracking, needle and target template images are updated with a template update strategy. Also, tracking outputs of normalized cross correlation and mutual information are fused using the Kalman filter to reduce the tracking error. During the experiments, needle is inserted using a needle insertion robot. 2D US probe is attached to a robotic arm's end effector to servo the probe along the needle insertion path. Proposed needle and target tracking methods were tested with phantoms. Accuracies of the needle tip and moving target tracking methods were measured using an optical tracking system. Experimental results showed that the proposed tracking method could be used to simultaneously track the needle tip and the targets in real-time in 2D US guided percutaneous needle procedures.TÜBİTA

Cell-based medicinal products (CBMPs) are rapidly gaining importance in the treatment of life-threatening diseases. However, the analytical toolbox for characterization of CBMPs is limited. The aim of our study was to develop a method based on flow imaging microscopy (FIM) for the detection, quantification and characterization of subvisible particulate impurities in CBMPs. Image analysis was performed by using an image classification approach based on a convolutional neural network (CNN). Jurkat cells and Dynabeads were used in our study as a representation of cellular material and non-cellular particulate impurities, respectively. We demonstrate that FIM assisted with CNN is a powerful method for the detection and quantification of Dynabeads and cells with other process related impurities, such as cell agglomerates, cell-bead adducts and debris. By using CNN, we achieved a more than 50-fold lower misclassification rate compared with the use of output parameters from the FIM software. The limit of detection was~15 000 beads/mL in the presence of 500 000 cells/mL, making this approach suitable for the detection of these particulate impurities in CBMPs. In conclusion, CNN-assisted FIM is a powerful method for the detection and quantification of cells, Dynabeads and other subvisible process impurities potentially present in CBMPs.