scholarly journals Clinical Evaluation of a Fully-automatic Segmentation Method for Longitudinal Brain Tumor Volumetry

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Raphael Meier ◽  
Urspeter Knecht ◽  
Tina Loosli ◽  
Stefan Bauer ◽  
Johannes Slotboom ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Clinical Evaluation ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Fully Automatic ◽  
Tumor Volumetry

SU-E-J-50: Fully Automatic Segmentation of Brain Tumor in CT Images

2011 ◽  
Vol 38 (6Part8) ◽  
pp. 3453-3453
Author(s):  
M Gao ◽  
D Wei ◽  
S Chen
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Automatic

A Hybrid Approach for Fast 3D Segmentation of Brain Tumor

2020 ◽  
Vol 17 (1) ◽  
pp. 340-346
Author(s):  
Ankur Biswas ◽  
Nitai Debnath ◽  
Debasish Datta ◽  
Sushanta Das ◽  
Paritosh Bhattacharya
Keyword(s):  
Brain Tumor ◽  
Human Brain ◽  
Spatial Data ◽  
Automatic Segmentation ◽  
Hybrid Approach ◽  
Region Growing ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Brain Tumor Segmentation ◽  
Need To Evaluate

Brain tumor segmentation and its study are tricky assignments of medical image processing due to complexity and variance of tumors however, forms a decisive factor for quantitative exploration of the spatial data in magnetic resonance imaging of human brain. In that mode, this modality of image has developed into a valuable investigative means in medicinal domain for detecting irregularity and discrepancy in human brain. The accuracy of segmentation method relies on its capability to discriminate different tissue, classes, discretely. Consequently there is an essential need to evaluate this capability prior to employing the segmentation method on medical images. In this paper, a semi-automatic segmentation technique is proposed to carry out the analysis and study of proficient pathologies of brain tumor of human brain. The task of segmentation is carried out integrating region growing with active contour methodologies. The evaluation of proposed methodology has been carried out on multislice image of MRI data and compared with other semi automatic and automatic techniques. It is observed by the experimental results that proposed system has the ability to accomplish fast segmentation and exact modeling of tumors in brain with a gratifying accuracy in order to support future treatment planning.

Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material to Objective Evaluation of Bowel Preparation Quality in Colonoscopy (Preprint)

2021 ◽  
Author(s):  
Yen-Po Wang ◽  
Ying-Chun Jheng ◽  
Kuang-Yi Sung ◽  
Hung-En Lin ◽  
I-Fang Hsin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Automatic Segmentation ◽  
Objective Evaluation ◽  
Manual Segmentation ◽  
Segmentation Method ◽  
Bowel Cleansing ◽  
Fully Automatic ◽  
Colon Preparation ◽  
Overlap Area

BACKGROUND Adequate bowel cleansing is important for a complete examination of the colon mucosa during colonoscopy. Current bowel cleansing evaluation scales are subjective with a wide variation in consistency among physicians and low reported rate. Artificial intelligence (AI) has been increasingly used in endoscopy. OBJECTIVE We aim to use machine learning to develop a fully automatic segmentation method to mark the fecal residue-coated mucosa for objective evaluation of the adequacy of colon preparation. METHODS Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation and verification datasets. The fecal residue was manually segmented by skilled technicians. Deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. TheA total of 10,118 qualified images from 119 videos were captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue. performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. RESULTS A total of 10,118 qualified images from 119 videos were captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue. CONCLUSIONS We used machine learning to establish a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for objective evaluation of colon preparation.

scholarly journals Fully Connected Pyramid Pooling Network (FCPPN) – A Method For Brain Tumor Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 7036-7041
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Semantic Segmentation ◽  
Tumor Segmentation ◽  
Performance Measurements ◽  
Brain Tumor Segmentation ◽  
Hybrid Combination ◽  
Fully Automatic ◽  
Fully Connected ◽  
The Brain

The procedure of separating the tumor from ordinary cerebrum images is called as brain tumor Segmentation . In segmenting the tumor it allows us to visualize the size and position of tumor within the brain.In Manual segmentation there is less accuracy so there is a need for fully automatic segmentation. A fully automatic segmentation called Semantic segmentation is a technique that classifies all the pixels of an image into meaningful classes of objects. Semantic Segmentation is mainly used in the area of medical imaging. It is mainly used for the doctors to identify the tumor in a clear and exact way. In this paper, we propose a new way of semantic segmentation technique to separate the tumor from the brain . The methods like Segnet, FCN, PSPNET are used for fully automatic segmentation and are used to predicate all types of Tumor. These methods are used to predicate the tumor.Our paper proposes a new architecture called FCPPNET which is a hybrid combination of FCN and PSPNET. Our proposed strategy is assessed utilizing Performance measurements, for example, the Dice coefficient, Accuracy, Sensitivity, and the outcomes appear to be more productive than the current strategies.

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.

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