C-Port Flex-A-assisted automated anastomosis for high-flow extracranial-intracranial bypass surgery in patients with symptomatic carotid artery occlusion: a feasibility study. Clinical article

OBJECT: Preliminary experience with the C-Port Flex-A Anastomosis System (Cardica, Inc.) to enable rapid automated anastomosis has been reported in coronary artery bypass surgery. The goal of the current study was to define the feasibility and safety of this method for high-flow extracranial-intracranial (EC-IC) bypass surgery in a clinical series. METHODS: In a prospective study design, patients with symptomatic carotid artery (CA) occlusion were selected for C-Port-assisted high-flow EC-IC bypass surgery if they met the following criteria: 1) transient or moderate permanent symptoms of focal ischemia; 2) CA occlusion; 3) hemodynamic instability; and 4) had provided informed consent. Bypasses were done using a radial artery graft that was proximally anastomosed to the superficial temporal artery trunk, the cervical external, or common CA. All distal cerebral anastomoses were performed on M2 branches using the C-Port Flex-A system. RESULTS: Within 6 months, 10 patients were enrolled in the study. The distal automated anastomosis could be accomplished in all patients; the median temporary occlusion time was 16.6+/-3.4 minutes. Intraoperative digital subtraction angiography (DSA) confirmed good bypass function in 9 patients, and in 1 the anastomosis was classified as fair. There was 1 major perioperative complication that consisted of the creation of a pseudoaneurysm due to a hardware problem. In all but 1 case the bypass was shown to be patent on DSA after 7 days; furthermore, in 1 patient a late occlusion developed due to vasospasm after a sylvian hemorrhage. One-week follow-up DSA revealed transient asymptomatic extracranial spasm of the donor artery and the radial artery graft in 1 case. Two patients developed a limited zone of infarction on CT scanning during the follow-up course. CONCLUSIONS: In patients with symptomatic CA occlusion, C-Port Flex-A-assisted high-flow EC-IC bypass surgery is a technically feasible procedure. The system needs further modification to achieve a faster and safer anastomosis to enable a conclusive comparison with standard and laser-assisted methods for high-flow bypass surgery.

Space–oriented EEG segmentation reveals changes in brain electric–field maps under the influence of a nootropic drug

Map landscape-based segmentation of the sequences of momentary potential distribution maps (42-channel recordings) into brain microstates during spontaneous brain activity was used to study brain electric field spatial effects of single doses of piracetam (2.9, 4.8, and 9.6 g Nootropil® UCB and placebo) in a double-blind study of five normal young volunteers. Four 15-second epochs were analyzed from each subject and drug condition. The most prominent class of microstates (covering 49% of the time) consisted of potential maps with a generally anterior-posterior field orientation. The map orientation of this microstate class showed an increasing clockwise deviation from the placebo condition with increasing drug doses (Fisher's probability product, p < 0.014). The results of this study suggest the use of microstate segmentation analysis for the assessment of central effects of medication in spontaneous multichannel electroencephalographic data, as a complementary approach to frequency-domain analysis.

Comparison of paired serum and lithium heparin plasma samples for the measurement of serum amyloid A in horses using an automated turbidimetric immunoassay

This study aimed to evaluate whether equine serum amyloid A (SAA) concentrations could be reliably measured in plasma with a turbidimetric immunoassay previously validated for equine SAA concentrations in serum. Paired serum and lithium-heparin samples obtained from 40 horses were evaluated. No difference was found in SAA concentrations between serum and plasma using a paired t test (P=0.48). The correlation between paired samples was 0.97 (Spearman's rank P<0.0001; 95% confidence interval 0.95-0.99). Passing-Bablok regression analyses revealed no differences between paired samples. Bland-Altman plots revealed a positive bias in plasma compared to serum but the difference was not considered clinically significant. The results indicate that lithium-heparin plasma samples are suitable for measurement of equine SAA using this method. Use of either serum or plasma allows for greater flexibility when it comes to sample collection although care should be taken when comparing data between measurements from different sample types.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Integrated Segmentation of Brain Tumor Images for Radiotherapy and Neurosurgery

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Fully automatic X-ray image segmentation via joint estimation of image displacements.

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

Statistical model-based segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs.

PURPOSE    Segmentation of the proximal femur in digital antero-posterior (AP) pelvic radiographs is required to create a three-dimensional model of the hip joint for use in planning and treatment. However, manually extracting the femoral contour is tedious and prone to subjective bias, while automatic segmentation must accommodate poor image quality, anatomical structure overlap, and femur deformity. A new method was developed for femur segmentation in AP pelvic radiographs. METHODS    Using manual annotations on 100 AP pelvic radiographs, a statistical shape model (SSM) and a statistical appearance model (SAM) of the femur contour were constructed. The SSM and SAM were used to segment new AP pelvic radiographs with a three-stage approach. At initialization, the mean SSM model is coarsely registered to the femur in the AP radiograph through a scaled rigid registration. Mahalanobis distance defined on the SAM is employed as the search criteria for each annotated suggested landmark location. Dynamic programming was used to eliminate ambiguities. After all landmarks are assigned, a regularized non-rigid registration method deforms the current mean shape of SSM to produce a new segmentation of proximal femur. The second and third stages are iteratively executed to convergence. RESULTS    A set of 100 clinical AP pelvic radiographs (not used for training) were evaluated. The mean segmentation error was [Formula: see text], requiring [Formula: see text] s per case when implemented with Matlab. The influence of the initialization on segmentation results was tested by six clinicians, demonstrating no significance difference. CONCLUSIONS    A fast, robust and accurate method for femur segmentation in digital AP pelvic radiographs was developed by combining SSM and SAM with dynamic programming. This method can be extended to segmentation of other bony structures such as the pelvis.

Robust Proximal Femur Segmentation in Conventional X-Ray Images via Random Forest Regression on Multi-resolution Gradient Features

In this paper, we propose a fully automatic, robust approach for segmenting proximal femur in conventional X-ray images. Our method is based on hierarchical landmark detection by random forest regression, where the detection results of 22 global landmarks are used to do the spatial normalization, and the detection results of the 59 local landmarks serve as the image cue for instantiation of a statistical shape model of the proximal femur. To detect landmarks in both levels, we use multi-resolution HoG (Histogram of Oriented Gradients) as features which can achieve better accuracy and robustness. The efficacy of the present method is demonstrated by experiments conducted on 150 clinical x-ray images. It was found that the present method could achieve an average point-to-curve error of 2.0 mm and that the present method was robust to low image contrast, noise and occlusions caused by implants.

Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

A Hybrid Model for Multimodal Brain Tumor Segmentation

We present a fully automatic segmentation method for multi-modal brain tumor segmentation. The proposed generative-discriminative hybrid model generates initial tissue probabilities, which are used subsequently for enhancing the classi�cation and spatial regularization. The model has been evaluated on the BRATS2013 training set, which includes multimodal MRI images from patients with high- and low-grade gliomas. Our method is capable of segmenting the image into healthy (GM, WM, CSF) and pathological tissue (necrotic, enhancing and non-enhancing tumor, edema). We achieved state-of-the-art performance (Dice mean values of 0.69 and 0.8 for tumor subcompartments and complete tumor respectively) within a reasonable timeframe (4 to 15 minutes).

True four-dimensional analysis of thoracic aortic displacement and distension using model-based segmentation of computed tomography angiography.

Previous analyses of aortic displacement and distension using computed tomography angiography (CTA) were performed on double-oblique multi-planar reformations and did not consider through-plane motion. The aim of this study was to overcome this limitation by using a novel computational approach for the assessment of thoracic aortic displacement and distension in their true four-dimensional extent. Vessel segmentation with landmark tracking was executed on CTA of 24 patients without evidence of aortic disease. Distension magnitudes and maximum displacement vectors (MDV) including their direction were analyzed at 5 aortic locations: left coronary artery (COR), mid-ascending aorta (ASC), brachiocephalic trunk (BCT), left subclavian artery (LSA), descending aorta (DES). Distension was highest for COR (2.3 ± 1.2 mm) and BCT (1.7 ± 1.1 mm) compared with ASC, LSA, and DES (p < 0.005). MDV decreased from COR to LSA (p < 0.005) and was highest for COR (6.2 ± 2.0 mm) and ASC (3.8 ± 1.9 mm). Displacement was directed towards left and anterior at COR and ASC. Craniocaudal displacement at COR and ASC was 1.3 ± 0.8 and 0.3 ± 0.3 mm. At BCT, LSA, and DES no predominant displacement direction was observable. Vessel displacement and wall distension are highest in the ascending aorta, and ascending aortic displacement is primarily directed towards left and anterior. Craniocaudal displacement remains low even close to the left cardiac ventricle.

Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting

In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%

Automated low flow pump system for the treatment of refractory ascites: A multi-center safety and efficacy study

BACKGROUND & AIMS: Refractory ascites (RA) affects 10% of patients with advanced cirrhosis and ascites. Usual therapy includes large volume paracentesis, and in selected patients, a transjugular portosystemic shunt (TIPS). These therapies may be associated with increased morbidity: paracentesis may induce circulatory dysfunction and impair quality of life and TIPS may induce encephalopathy and is associated with increased mortality in patients with severe liver dysfunction. We present the results of a multicenter, non-randomized trial to assess the safety and efficacy of a new automated pump system for treatment of RA. METHODS: Forty patients at 9 centers (February 2010-June 2011) received an implanted pump for the automated removal of ascites from the peritoneal cavity into the bladder, from where it was eliminated through normal urination. Patients were followed-up for 6months. The primary study outcome was safety. Secondary outcomes included recurrence of tense ascites and pump performance. RESULTS: Surgical complications occurred early in the study and became less frequent. The pump system removed 90% of the ascites and significantly reduced the median number of large volume paracentesis per month [3.4 (range 1-6) vs. 0.2 (range 0-4); p <0.01]. Cirrhosis-related adverse events decreased along follow-up. CONCLUSIONS: The automated pump seems an efficacious tool to move out ascites from the peritoneal cavity to the bladder. Its safety is still moderate, but a broad use in different countries will improve the surgical technique as well as the medical surveillance. A prospective randomized clinical trial vs. large volume paracentesis is underway to confirm these preliminary results.