scholarly journals Nearly automatic vessels segmentation using graph-based energy minimization

2009 ◽  
Author(s):  
Moti Freiman ◽  
Judith Frank ◽  
Lior Weizman ◽  
Einav Nammer ◽  
Ofek Shilon ◽  
...  
Keyword(s):  
Shortest Path ◽  
Energy Minimization ◽  
Region Of Interest ◽  
Ground Truth ◽  
Minimization Method ◽  
Anatomical Structures ◽  
Vascular Structures ◽  
Automatic Tool ◽  
Symmetric Surface ◽  
Seed Points

We present a nearly automatic tool for the accurate segmentation of vascular structures in volumetric CTA images. Its inputs are a start and an end seed points inside the vessel. The two-step graph-based energy minimization method starts by computing the weighted shortest path between the vessel seed endpoints based on local image and seed intensities and vessel path geometric characteristics. It then automatically defines a Vessel Region Of Interest (VROI) from the shortest path and the estimated vessel radius, and extracts the vessels boundaries by minimize the energy on a corresponding graph cut.We evaluate our method within the 2009 MICCAI 3D Segmentation Challenge for Clinical Applications Workshop. Experimental results on the 46 carotid bifurcations from clinical CTAs, compared to ground-truth genrated by averaging three manual annotations, yield an average symmetric surface distance of 0.83mm and a Dice similarity of 81.8%, with only three input seeds. These results indicates that our method is easy to use, produces accurate segmentations of vessels lumen, and is robust to intensity variations inside the vessels, radius changes, bifurcations, and nearby anatomical structures with similar intensity values.

scholarly journals A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Huiyan Jiang ◽  
Baochun He ◽  
Di Fang ◽  
Zhiyuan Ma ◽  
Benqiang Yang ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Region Growing ◽  
Vessel Segmentation ◽  
Growth Strategy ◽  
Segmentation Algorithm ◽  
Edge Information ◽  
Manual Intervention ◽  
Vascular Structures ◽  
Region Growing Segmentation ◽  
Seed Points

We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure’s center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.

Extraction of the Coefficient of Thermal Expansion of Thin Films from Buckled Membranes

1998 ◽  
Vol 546 ◽  
Author(s):  
V. Ziebartl ◽  
O. Paul ◽  
H. Baltes
Keyword(s):  
Thin Films ◽  
Finite Element ◽  
Thermal Expansion ◽  
Silicon Nitride ◽  
Excellent Agreement ◽  
Energy Minimization ◽  
Coefficient Of Thermal Expansion ◽  
Temperature Dependent ◽  
Minimization Method ◽  
Energy Minimization Method

AbstractWe report a new method to measure the temperature-dependent coefficient of thermal expansion α(T) of thin films. The method exploits the temperature dependent buckling of clamped square plates. This buckling was investigated numerically using an energy minimization method and finite element simulations. Both approaches show excellent agreement even far away from simple critical buckling. The numerical results were used to extract Cα(T) = α0+α1(T−T0 ) of PECVD silicon nitride between 20° and 140°C with α0 = (1.803±0.006)×10−6°C−1, α1 = (7.5±0.5)×10−9 °C−2, and T0 = 25°C.

Open Source Software Implementation of Anatomical Segmentation

2018 ◽  
Author(s):  
Maggie Hess
Keyword(s):  
Large Scale ◽  
Focused Ultrasound ◽  
Region Growing ◽  
Ground Truth ◽  
Clot Lysis ◽  
Focused Ultrasound Surgery ◽  
Manual Methods ◽  
Areas Of Interest ◽  
Seed Points ◽  
Viable Method

Purpose: Intraventricular hemorrhage (IVH) affects nearly 15% of preterm infants. It can lead to ventricular dilation and cognitive impairment. To ablate IVH clots, MR-guided focused ultrasound surgery (MRgFUS) is investigated. This procedure requires accurate, fast and consistent quantification of ventricle and clot volumes. Methods: We developed a semi-autonomous segmentation (SAS) algorithm for measuring changes in the ventricle and clot volumes. Images are normalized, and then ventricle and clot masks are registered to the images. Voxels of the registered masks and voxels obtained by thresholding the normalized images are used as seed points for competitive region growing, which provides the final segmentation. The user selects the areas of interest for correspondence after thresholding and these selections are the final seeds for region growing. SAS was evaluated on an IVH porcine model.  Results: SAS was compared to ground truth manual segmentation (MS) for accuracy, efficiency, and consistency. Accuracy was determined by comparing clot and ventricle volumes produced by SAS and MS. In Two-One-Sided Test, SAS and MS were found to be significantly equivalent (p < 0.01). SAS on average was found to be 15 times faster than MS (p < 0.01). Consistency was determined by repeated segmentation of the same image by both SAS and manual methods, SAS being significantly more consistent than MS (p < 0.05).  Conclusion: SAS is a viable method to quantify the IVH clot and the lateral brain ventricles and it is serving in a large- scale porcine study of MRgFUS treatment of IVH clot lysis.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

scholarly journals GPS and Stereovision-Based Visual Odometry: Application to Urban Scene Mapping and Intelligent Vehicle Localization

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Lijun Wei ◽  
Cindy Cappelle ◽  
Yassine Ruichek ◽  
Frédérick Zann
Keyword(s):  
Feature Detection ◽  
Reference Frames ◽  
Estimation Method ◽  
Ground Truth ◽  
Urban Environments ◽  
Relative Depth ◽  
Gps Receiver ◽  
Minimization Method ◽  
Vehicle Localization ◽  
Stereoscopic System

We propose an approach for vehicle localization in dense urban environments using a stereoscopic system and a GPS sensor. Stereoscopic system is used to capture the stereo video flow, to recover the environments, and to estimate the vehicle motion based on feature detection, matching, and triangulation from every image pair. A relative depth constraint is applied to eliminate the tracking couples which are inconsistent with the vehicle ego-motion. Then the optimal rotation and translation between the current and the reference frames are computed using an RANSAC based minimization method. Meanwhile, GPS positions are obtained by an on-board GPS receiver and periodically used to adjust the vehicle orientations and positions estimated by stereovision. The proposed method is tested with two real sequences obtained by a GEM vehicle equipped with a stereoscopic system and a RTK-GPS receiver. The results show that the vision/GPS integrated trajectory can fit the ground truth better than the vision-only method, especially for the vehicle orientation. And vice-versa, the stereovision-based motion estimation method can correct the GPS signal failures (e.g., GPS jumps) due to multipath problem or other noises.

Rapid Central Vein Assessment (RaCeVA): A systematic, standardized approach for ultrasound assessment before central venous catheterization

2018 ◽  
Vol 20 (3) ◽  
pp. 239-249 ◽  
Author(s):  
Timothy R Spencer ◽  
Mauro Pittiruti
Keyword(s):  
Vascular Access ◽  
Central Venous Catheterization ◽  
Central Vein ◽  
Arterial Cannulation ◽  
Central Venous ◽  
Anatomical Structures ◽  
Venous Catheterization ◽  
Ultrasound Technology ◽  
Ultrasound Assessment ◽  
Vascular Structures

Ultrasound technology has revolutionized the practice of safer vascular access, for both venous and arterial cannulation. The ability to visualize underlying structures of the chest, neck, and upper/lower extremities provides for greater success, speed, and safety with all vascular access procedures. Ultrasound not only yields superior procedural advantages but also provides a platform to perform a thorough assessment of the vascular structures to evaluate vessel health, viability, size, and patency, including the location of other important and best avoided anatomical structures—prior to performing any procedures. Such assessment is best performed using a systematic and standardized approach, as the Rapid Central Vein Assessment, described in this study.

scholarly journals A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bo Chen ◽  
Yan Lv ◽  
Jinbin Zou ◽  
Wensheng Chen ◽  
Binbin Pan
Keyword(s):  
Energy Minimization ◽  
Speckle Noise ◽  
Noise Removal ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Minimization Method ◽  
Alternating Direction ◽  
Step Effect ◽  
New Energy ◽  
Series Of Experiments

Speckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new energy minimization model’s solution are proved. Series of experiments are designed in this paper. Numerical results show that the new algorithm can reduce the step effect effectively obtain good results in visual effect and quantitative measures by comparing with some traditional models.

Effect of CaF2 in Precursors on Steel Pipe Lined Fe-Al Intermetallic-TiB2-Al2O3 Composite Coating by Centrifugal-SHS Process

2012 ◽  
Vol 488-489 ◽  
pp. 468-472 ◽  
Author(s):  
Pajaree Kerdkool ◽  
Sutham Niyomwas
Keyword(s):  
Energy Minimization ◽  
Standard Gibbs Energy ◽  
Raw Materials ◽  
Steel Pipe ◽  
X Ray Diffraction ◽  
Temperature Synthesis ◽  
Minimization Method ◽  
X Ray ◽  
High Temperature Synthesis ◽  
Centrifugal Shs

Steel pipe lined Fe-Al intermetallic-TiB2-Al2O3composite were prepared by centrifugal-self-propagating high temperature synthesis (centrifugal-SHS) process from FeTiO3, B2O3, Fe2O3, Al and CaF2as raw materials. The standard Gibbs energy minimization method was used to calculate the equilibrium compositions of the reacting species. The effect of adding CaF2to the precursors on the result product were investigated. The phase separation between less porosity Fe-Al intermetallics-TiB2with Al2O3layer were affected greatly by adding CaF2. The phase compositions and morphology were characterized by X-ray diffraction (XRD) and scanning electron microscope (SEM) couple with energy dispersive X-ray (EDX), respectively.

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