Abstract Recently, there is a growing interest in surgical variables that are intraoperatively controlled by orthopaedic surgeons, including lower leg alignment, component positioning and soft tissues balancing. Since more tight control over these factors is associated with improved outcomes of unicompartmental knee arthroplasty and total knee arthroplasty (TKA), several computer navigation and robotic-assisted systems have been developed. Although mechanical axis accuracy and component positioning have been shown to improve with computer navigation, no superiority in functional outcomes has yet been shown. This could be explained by the fact that many differences exist between the number and type of surgical variables these systems control. Most systems control lower leg alignment and component positioning, while some in addition control soft tissue balancing. Finally, robotic assisted systems have the additional advantage of improving surgical precision. A systematic search in PubMed, Embase and Cochrane Library resulted in 40 comparative studies and three registries on computer navigation reporting outcomes of 474,197 patients, and 21 basic science and clinical studies on robotic-assisted knee arthroplasty. Twenty-eight of these comparative computer navigation studies reported Knee Society Total scores in 3504 patients. Stratifying by type of surgical variables, no significant differences were noted in outcomes between surgery with computer-navigated TKA controlling for alignment and component positioning versus conventional TKA (p = 0.63). However, significantly better outcomes were noted following computer-navigated TKA that also controlled for soft tissue balancing versus conventional TKA (mean difference 4.84, 95 % Confidence Interval 1.61, 8.07, p = 0.003). A literature review of robotic systems showed that these systems can, similarly to computer navigation, reliably improve lower leg alignment, component positioning and soft tissues balancing. Furthermore, two studies comparing robotic-assisted with computer-navigated surgery reported superiority of robotic-assisted surgery in controlling these factors. Manually controlling all these surgical variables can be difficult for the orthopaedic surgeon. Findings in this study suggest that computer navigation or robotic assistance may help managing these multiple variables and could improve outcomes. Future studies assessing the role of soft tissue balancing in knee arthroplasty and long-term follow-up studies assessing the role of computer-navigated and robotic assisted knee arthroplasty are needed.
Keywords Computer navigation · Robotics · Unicompartmental knee arthroplasty · Total knee arthroplasty · Soft tissue balancing
We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm consists of four steps: 1) deformable registration of the baseline scan and tumors delineations to the followup CT scan; 2) automatic segmentation of the liver; 3) training a Convolutional Neural Network (CNN) as a voxel classifier on all baseline; 4) segmentation of the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield an average overlap error of 16.26% (std=10.33).
Abstract—Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud–based evaluation framework is presented in this paper including results of benchmarking current state–of–the–art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud that can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set, where participants can only access the training data. Overall 80 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, the resulting data set, evaluation setup, results and the performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. The VISCERAL data set and silver corpus generated with the fusion of the participant algorithms on a larger set of non–manually–annotated medical images are available to the research community.
Index Terms—Evaluation framework, organ segmentation, landmark detection.
Purpose The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems.
Methods We present a newmethod for incorporating textual radiological findings from medical case reports in M-CBIR.The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy–Pathology List. It uses a new case relatednessmetric relCase that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case.
Results An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods.
Conclusions The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.
Keywords Medical Content-Based Image Retrieval. RadLex ontology . Similarity metric . Relatedness
We present a new method for rigid registration of CT datasets in 3D Radon space based on sparse sampling of scanning projections. The inputs are the two 3D Radon transforms of the CT scans, one densely sampled and the other sparsely sampled (limited number of scan angles/ranges). The output is the rigid transformation that best matches them. The method first finds the best matching between each projection direction vector in the sparse transform and the corresponding direction vector in the dense transform. It then solves a system of linear equations derived from the direction vector pairs (parallel-beam projections) or finds a solution by non-linear optimization (fan-beam and cone-beam projections). Experimental studies show that our method for 3D parallel beam registration outperforms image space registration in terms of convergence range with significantly reduced X-ray dose compared to a full conventional CT scan.
Index Terms—CT scanning, reduced-dose scanning, rigid registration, Radon transform, image processing.
Computer Aided Orthopaedic Surgery (CAOS) is now about 25 years old. Unlike Neurosurgery, Computer Aided Surgery has not become the standard of care in Orthopaedic Surgery. In this paper, we provide the technical and clinical context raised by this observation in an attempt to elucidate the reasons for this state of affairs. We start with a brief outline of the history of CAOS, review the main CAOS technologies, and describe how they are evaluated. We then identify some of the current publications in the field and present the opposing views on their clinical impact and their acceptance by the orthopaedic community worldwide. We focus on total knee replacement surgery as a case study and present current clinical results and contrasting opinions on CAOS technologies. We then discuss the challenges and opportunities for research in medical image analysis in CAOS and in musculoskeletal radiology. We conclude with a suggestion that while CAOS acceptance may be more moderate than that of other fields in surgery, it still has a place in the arsenal of useful tools available to orthopaedic surgeons.
Surgical accuracy is multifactorial – it is therefore crucial to take into account all influencing factors when investigating the accuracy of a surgical gesture, such as the surgeon’s experience, local difficulties associated with the specific anatomical site and the assistive technologies that may be used by the surgeon. For in-vitro pre-clinical investigations, accuracy should be linked to the concepts of trueness, e.g. distance from the surgical target, and precision, e.g. variability in relation to the surgical target, to gather pre-clinical, quantitative, objective data on the accuracy of completed surgical procedures when using assistive technologies. The clinical relevance of improvements in accuracy that have been observed experimentally may be evaluated by analysing the impact on the risk of failure and by taking into account the level of tolerance in relation to the surgical target, e.g. the extent of the safety zone. The ISO methodology enables the pre-clinical testing of new assistive technologies to quantify improvements in accuracy and assess the benefits in terms of reducing the risk of failure and achieving surgical targets with tighter tolerances before the testing of clinical outcomes.