No abstract

Segmentation of the wrist bones in CT images has been frequently used in different clinical applications including arthritis evaluation, bone age assessment and image-guided interventions. The major challenges include non-uniformity and spongy textures of the bone tissue as well as narrow inter-bone spaces. In this work, we propose an automatic wrist bone segmentation technique for CT images based on a statistical model that captures the shape and pose variations of the wrist joint across 60 example wrists at nine different wrist positions. To establish the correspondences across the training shapes at neutral positions, the wrist bone surfaces are jointly aligned using a group-wise registration framework based on a Gaussian Mixture Model. Principal component analysis is then used to determine the major modes of shape variations. The variations in poses not only across the population but also across different wrist positions are incorporated in two pose models. An intra-subject pose model is developed by utilizing the similarity transforms at all wrist positions across the population. Further, an inter-subject pose model is used to model the pose variations across different wrist positions. For segmentation of the wrist bones in CT images, the developed model is registered to the edge point cloud extracted from the CT volume through an expectation maximization based probabilistic approach. Residual registration errors are corrected by application of a non-rigid registration technique. We validate the proposed segmentation method by registering the wrist model to a total of 66 unseen CT volumes of average voxel size of 0.38 mm. We report a mean surface distance error of 0.33 mm and a mean Jaccard index of 0.86.

The standard workflow in many image-guided procedures, preoperative imaging followed by intraoperative registration, can be a challenging process and is not readily adaptable to certain anatomical regions such as the wrist. In this study we present an alternative, consisting of a preoperative registration calibration and intraoperative navigation using 3D cone-beam CT. A custom calibration tool was developed to preoperatively register an optical tracking system to the imaging space of a digital angiographic C-arm. This preoperative registration was then applied to perform direct navigation using intraoperatively acquired images for the purposes of an in-vitro wrist fixation procedure. A validation study was performed to assess the stability of the registration and found that the mean registration error was approximately 0.3 mm. When compared to two conventional techniques, our navigated wrist repair achieved equal or better screw placement, with fewer drilling attempts and no additional radiation exposure to the patient. These studies suggest that preoperative registration coupled with direct navigation using procedurespecific graphical rendering, is potentially a highly accurate and effective means of performing image-guided interventions.

Our study examines written corrective feedback generated by two online grammar checkers (GCs), Grammarly and Virtual Writing Tutor, and by the grammar checking function of Microsoft Word. We tested the technology on a wide range of grammatical error types from two sources: a set of authentic ESL compositions and a series of simple sentences we generated ourselves. The GCs were evaluated in terms of (1) coverage (number of errors flagged), (2) appropriacy of proposed replacement forms, and (3) rates of “false alarms” (forms mistakenly flagged as incorrect). Although Grammarly and Virtual Writing Tutor outperformed Microsoft Word, neither of the online GCs had high rates of overall coverage (<50%). Consequently, they cannot be relied on to supply comprehensive feedback on student compositions. The finding of higher identification rates for errors from simple rather than authentic sentences reinforces this conclusion. Nonetheless, since few inaccurate replacement forms and false alarms were observed, only rarely is the feedback actively misleading. In addition, the GCs were better at handling some error types than others. Ultimately, we suggest that teachers use GCs with specially designed classroom activities that target selected error types before learners apply the technology to their own writing.

Volume-sliced navigation achieved a more repeatable and reliable central pin placement, with fewer drilling attempts than conventional 2D techniques. Volume-sliced navigation had a higher number of drill paths within the optimal zone maximizing both length of the path and depth from the surface.