Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

2012 ◽  
Vol 51 (05) ◽  
pp. 415-422 ◽  
Author(s):  
A. Schmidt-Richberg ◽  
J. Fiehler ◽  
T. Illies ◽  
D. Möller ◽  
H. Handels ◽  
...  
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Time Of Flight ◽  
Medical Expert ◽  
Consistency Check ◽  
Major Drawback ◽  
Segmentation Methods ◽  
Initial Segmentation ◽  
Level Set Approach ◽  
Manual Correction

Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

scholarly journals Segmentation of Meristem Cells by an Automated Optimization Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8523
Author(s):  
Oswaldo Rojas ◽  
Manuel G. Forero ◽  
José M. Menéndez ◽  
Angharad Jones ◽  
Walter Dewitte ◽  
...  
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Optimization Method ◽  
Intensity Level ◽  
Low Contrast ◽  
Segmentation Methods ◽  
Segmented Images ◽  
Optimization Function ◽  
Confocal Images ◽  
Automated Optimization

Meristem cells are irregularly shaped and appear in confocal images as dark areas surrounded by bright ones. Images are characterized by regions of very low contrast and absolute loss of edges deeper into the meristem. Edges are blurred, discontinuous, sometimes indistinguishable, and the intensity level inside the cells is similar to the background of the image. Recently, a technique called Parametric Segmentation Tuning was introduced for the optimization of segmentation parameters in diatom images. This paper presents a PST-tuned automatic segmentation method of meristem cells in microscopy images based on mathematical morphology. The optimal parameters of the algorithm are found by means of an iterative process that compares the segmented images obtained by successive variations of the parameters. Then, an optimization function is used to determine which pair of successive images allows for the best segmentation. The technique was validated by comparing its results with those obtained by a level set algorithm and a balloon segmentation technique. The outcomes show that our methodology offers better results than two free available state-of-the-art alternatives, being superior in all cases studied, losing 9.09% of the cells in the worst situation, against 75.81 and 25.45 obtained in the level set and the balloon segmentation techniques, respectively. The optimization method can be employed to tune the parameters of other meristem segmentation methods.

scholarly journals Automatic segmentation with detection of local segmentation failures in cardiac MRI

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jörg Sander ◽  
Bob D. de Vos ◽  
Ivana Išgum
Keyword(s):  
Automatic Segmentation ◽  
Magnetic Resonance Images ◽  
Anatomical Structures ◽  
Diagnosis And Prognosis ◽  
Fold Reduction ◽  
Segmentation Methods ◽  
Complete Set ◽  
And Performance ◽  
The Impact ◽  
Manual Correction

AbstractSegmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To increase robustness and performance of segmentation methods this study combines automatic segmentation and assessment of segmentation uncertainty in CMRI to detect image regions containing local segmentation failures. Three existing state-of-the-art convolutional neural networks (CNN) were trained to automatically segment cardiac anatomical structures and obtain two measures of predictive uncertainty: entropy and a measure derived by MC-dropout. Thereafter, using the uncertainties another CNN was trained to detect local segmentation failures that potentially need correction by an expert. Finally, manual correction of the detected regions was simulated in the complete set of scans of 100 patients and manually performed in a random subset of scans of 50 patients. Using publicly available CMR scans from the MICCAI 2017 ACDC challenge, the impact of CNN architecture and loss function for segmentation, and the uncertainty measure was investigated. Performance was evaluated using the Dice coefficient, 3D Hausdorff distance and clinical metrics between manual and (corrected) automatic segmentation. The experiments reveal that combining automatic segmentation with manual correction of detected segmentation failures results in improved segmentation and to 10-fold reduction of expert time compared to manual expert segmentation.

Two-phase flow stability analysis using a level set approach

2017 ◽  
Author(s):  
Mamta Raju Jotkar ◽  
Daniel Rodriguez ◽  
Bruno Marins Soares
Keyword(s):  
Stability Analysis ◽  
Level Set ◽  
Two Phase Flow ◽  
Flow Stability ◽  
Phase Flow ◽  
Two Phase ◽  
Level Set Approach

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

A versatile algorithm for the automatic segmentation of hippocampus based on level set

2011 ◽  
Vol 7 (3) ◽  
pp. 213 ◽  
Author(s):  
J. Rajeesh ◽  
R.S. Moni ◽  
S. Palanikumar ◽  
T. Gopalakrishnan
Keyword(s):  
Level Set ◽  
Automatic Segmentation
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