To Learn or Not to Learn Features for Deformable Registration?

Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-of-the-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights into the type of DL framework useful for feature learning and the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features.Comment: 9 pages, 4 figure

Intensity-Based Registration of Freehand 3D Ultrasound and CT-scan Images of the Kidney

This paper presents a method to register a pre-operative Computed-Tomography (CT) volume to a sparse set of intra-operative Ultra-Sound (US) slices. In the context of percutaneous renal puncture, the aim is to transfer planning information to an intra-operative coordinate system. The spatial position of the US slices is measured by optically localizing a calibrated probe. Assuming the reproducibility of kidney motion during breathing, and no deformation of the organ, the method consists in optimizing a rigid 6 Degree Of Freedom (DOF) transform by evaluating at each step the similarity between the set of US images and the CT volume. The correlation between CT and US images being naturally rather poor, the images have been preprocessed in order to increase their similarity. Among the similarity measures formerly studied in the context of medical image registration, Correlation Ratio (CR) turned out to be one of the most accurate and appropriate, particularly with the chosen non-derivative minimization scheme, namely Powell-Brent's. The resulting matching transforms are compared to a standard rigid surface registration involving segmentation, regarding both accuracy and repeatability. The obtained results are presented and discussed

Navigated cup implantation in hip arthroplasty: A meta-analysis

Background and purpose Many studies have suggested that navigation-based implantation can improve cup positioning in total hip arthroplasty (THA). We conducted a systematic review and meta-analysis to compile the best available evidence, and to overcome potential shortcomings because of small sample sizes in individual studies

Focused registration of tracked 2D US to 3D CT data of the liver

The paper deals with the registration of pre-operative 3DCT- data to tracked intra-operative 2D-US-slices in the context of liver surgery. To bring such a method to clinical practice, it has to be fast and robust. In order to meet these demanding criteria, we propose two strategies. Instead of applying a time-consuming compounding process to obtain a 3D-US image, we use the 2D-slices directly and thereby drastically reduce the complexity and enhance the robustness of the scheme. Naturally, the surgeon does not need the same high resolution for the whole liver. We make use of this fact by applying a focusing technique to regions of special interest. With this, we reduce the overall amount of data to register significantly without sacrificing the accuracy in the ROIs. In contrast to other attempts, the high resolution result in the ROI is combined in a natural way with a global deformation field to obtain a smooth registration of the whole liver. Overall we arrive at a method with a favorable timing. The proposed algorithm was applied to four different patient data-sets and evaluated with respect to the reached vessel-overlap on validation slices. The obtained results are very convincing and will help to bring non-linear registration techniques to the operation theater

Assessment of a technique for 2D-3D registration of cerebral intra-arterial angiography.

This study assesses the ability of a computer algorithm to perform automated 2D-3D registrations of digitally subtracted cerebral angiograms. The technique was tested on clinical studies of five patients with intracranial aneurysms. The automated procedure was compared against a gold standard manual registration, and achieved a mean registration accuracy of 1.3 mm (SD 0.6 mm). Two registration strategies were tested using coarse (128 x 128 pixel) or fine (256 x 256 pixel) images. The mean registration errors proved similar but registration of the lower resolution images was 3 times quicker (mean registration times 33 s, SD 13 s for low and 150 s SD 48 s for high resolution images). The automated techniques were considerably faster than manual registrations but achieved similar accuracy. The technique has several potential uses but is particularly applicable to endovascular treatment techniques