Computer Assisted Surgery最新文献

To improve the quality of the super-resolution (SR) reconstructed medical images, an improved adaptive multi-dictionary learning method is proposed, which uses the combined information of medical image itself and the natural images database. In training dictionary section, it uses the upper layer images of pyramid which are generated by the self-similarity of low resolution images. In reconstruction section, the top layer image of pyramid is taken as the initial reconstruction image, and medical image's SR reconstruction is achieved by regularization term which is the non-local structure self-similarity of the image. This method can make full use of the same scale and different scale similar information of medical images. Simulation experiments are carried out on natural images and medical images, and the experimental results show the proposed method is effective for improving the effect of medical image SR reconstruction.

ABSTARCT Real-time tool tracking in minimally invasive-surgery (MIS) has numerous applications for computer-assisted interventions (CAIs). Visual tracking approaches are a promising solution to real-time surgical tool tracking, however, many approaches may fail to complete tracking when the tracker suffers from issues such as motion blur, adverse lighting, specular reflections, shadows, and occlusions. We propose an automatic real-time method for two-dimensional tool detection and tracking based on a spatial transformer network (STN) and spatio-temporal context (STC). Our method exploits both the ability of a convolutional neural network (CNN) with an in-house trained STN and STC to accurately locate the tool at high speed. Then we compared our method experimentally with other four general of CAIs' visual tracking methods using eight existing online and in-house datasets, covering both in vivo abdominal, cardiac and retinal clinical cases in which different surgical instruments were employed. The experiments demonstrate that our method achieved great performance with respect to the accuracy and the speed. It can track a surgical tool without labels in real time in the most challenging of cases, with an accuracy that is equal to and sometimes surpasses most state-of-the-art tracking algorithms. Further improvements to our method will focus on conditions of occlusion and multi-instruments.

To examine the clinical results of different minimally invasive techniques for the therapy of far lateral disc herniation in middle-aged and elderly patients. An endoscopic approach (percutaneous endoscopic lumbar discectomy; PELD), MIS-TLIF combined with contralateral translaminar screw (MIS-TLIF CTS), and MIS-TLIF combined with bilateral pedicle screws (MIS-TLIF BPS) were evaluated via a retrospective chart review. Data from 74 consecutive middle-aged and elderly patients with far lateral disc herniation were analyzed. All patients underwent surgery; 19 with PELD, 24 with MIS-TLIF CTS, and 31 with MIS-TLIF BPS. Clinical data included the length of the incision, duration of the operation, estimated blood loss, hospitalization time, operation cost, recurrence rate, and fusion rate. Preoperative and postoperative patient outcomes including the VAS, ODI scores and MacNab criteria were assessed and recorded. The mean follow-up time was 26.4 months (range from 14 to 46 months). Compared with the internal fixation groups, the length of the incision, duration of operation, estimated blood loss, and hospitalization time were obviously lower in the PELD group. The difference in operation cost among the three methods was statistically significant. The postoperative VAS scores for LBP and LP decreased significantly as compared with those recorded preoperatively. The postoperative ODI scores were lower than those recorded preoperatively. MacNab criteria rating excellent, good and fair results were in 27, 37 and 10 patients, respectively. Conclusion: PELD, MIS-TLIF CTS, and MIS-TLIF BPS are all effective minimally invasive techniques for the therapy of single segment far lateral lumbar disc herniation in middle-aged and elderly patients. PELD had a shorter operation time and less surgical trauma, being a less invasive and more economical method; however, there was no recurrence of disc herniation after fixation. Compared with MIS-TLIF BPS, MIS-TLIF CTS obtained a similar clinical effect and certain costs were saved.

Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.

Color, texture, and shape are the common features used for the retrieval systems. However, many medical images have a spot of color information. Therefore, the discriminative texture and shape features should be extracted to obtain a satisfied retrieval result. In order to increase the credibility of the retrieval process, many features can be combined to be used for medical image retrieval. Meanwhile, more features require more processing time, which will decrease the retrieval speed. In this paper, wavelet decomposition is adopted to generate different resolution images. Bag-of-feature, texture, and LBP feature are extracted from three different-level wavelet images. Finally, the similarity measure function is obtained by fusing these three types of features. Experimental results show that the proposed multi-feature fusion method can achieve a higher retrieval accuracy with an acceptable retrieval time.

Hematoma enlargement often occurs in patients with spontaneous intracerebral hemorrhage (ICH), so it is necessary to monitor the amount of intracranial hemorrhage in patients after admission. At present, the commonly used intracranial pressure (ICP) method has the disadvantages of trauma and infection, and the Computer Tomography (CT) method cannot achieve continuous monitoring. So it is urgent to develop a non-contact and non-invasive method for continuous monitoring of cerebral hemorrhage. The dielectric properties of blood are different from those of brain tissue, so the hematoma will affect the amplitude and phase of the electromagnetic waves passing through the head. A microstrip antenna was designed to construct the detection system for cerebral hemorrhage. Based on the animal model of acute cerebral hemorrhage, the detecting experiment was carried out on thirteen rabbits. Each rabbit had three bleeding states: 1, 2, and 3 ml, which represented the severity of cerebral hemorrhage. According to the measured data of high dimension and small sample, the support vector machine (SVM) algorithm was used to assess the severity of cerebral hemorrhage. According to simulation results, the antenna's forward radiation was 5 dB larger than the backward radiation, which ensured the antenna being not affected by external signals during the measurement. According to test results, the -10 dB workband of the antenna was 1.55-2.05 GHz and the frequency range of the transmission parameters S₂₁ above -30 dB is 1.2 - 3 GHz. In the animal experiment, the phase difference of Transmission coefficient S₂₁ was gradually increased with the increase of bleeding volume. Through the classification of 39 bleeding states of the 13 rabbits, the total accuracy was about 77%. Through animal experiments, the feasibility of detection method has been proved. But the classification accuracy need to be further improved. The detection system is based on broadband antenna has the potential to realize non-contact, non-invasive and continuous monitoring for cerebral hemorrhage.

In general, the 3 D printed medical models are made based on virtual digital models obtained from machines such as the computed tomography scanner. However, due to the limited accuracy of CT scanning technology, which is usually 1 millimeter, there are differences between scanned results and the real structure. Besides, the collected data can hardly be printed directly because of some errors in the model. In this paper, we present a general and efficient procedure to process the digital skull data to make the printed structures meet the requirements of anatomy education, which combines the use of five 3 D manipulation tools and the procedure can be finished within 6 hours. Then the model is printed and compared with the cadaveric skull from frontal, left, right and anterior views respectively. The printed model can describe the correct structure and details of the skull clearly, which can be considered as a good alternative to the cadaveric skull. The manipulation procedure presented in this study is an easily available and cost-effective way to obtain a printed skull model from the original CT data, which has a considerable economic and social benefit for the medical education. The steps of the data processing can be performed easily. The cost for the 3 D printed model is also low. Outcomes of this study can be applied widely in processing skull data.

Minimally invasive laparoscopic surgery is associated with small wounds and short recovery time, reducing postoperative infections. Traditional two-dimensional (2D) laparoscopic imaging lacks depth perception and does not provide quantitative depth information, thereby limiting the field of vision and operation during surgery. However, three-dimensional (3D) laparoscopic imaging from 2 D images lets surgeons have a depth perception. However, the depth information is not quantitative and cannot be used for robotic surgery. Therefore, this study aimed to reconstruct the accurate depth map for binocular 3 D laparoscopy. In this study, an unsupervised learning method was proposed to calculate the accurate depth while the ground-truth depth was not available. Experimental results proved that the method not only generated accurate depth maps but also provided real-time computation, and it could be used in minimally invasive robotic surgery.

Simple fractal dimensions have been proposed for use in the analysis of the characteristics of digitized tongue pictures and tongue coating texture, which could further the establishment of objectified classification criteria under the conditions of expanding sample size. However, detailed descriptions on simple fractal dimensions have been limited. Therefore, BP (back propagation) neural network model classifiers could be designed by further calculation of the multiple fractal spectrum characteristics of digitized tongue pictures in order to classify and recognize the thin/thick or greasy characteristics of tongue coating. The fractal dimensions of sample data of 587 digitized tongue pictures were collected in a standard environment. A statistical analysis was conducted on the calculation results of the sample data, and the sensitivity of the fractal dimensions to the thin/thick and greasy characteristics of digitized tongue pictures was observed. As the overlap region resulted from a range of values of a single parameter, another 8 characteristic parameters of the multiple fractal spectra of the digitized tongue pictures were further proposed as the elements in the input layer of the three-layers BP neural network. Automatic recognition classifiers were designed and trained for the characteristics of digitized tongue pictures and tongue coating textures. The simple fractal dimension was sensitive to the thin/thick and greasy characteristics of digitized tongue pictures and could better judge the characteristics of the thickness of the tongue coating. A classifier with characteristic parameters of multiple fractal spectra as the input vectors identified by the BP neural network models could effectively increase the accuracy rate judged by the characteristics of the tongue coating texture.