scholarly journals Assessing the Accuracy and Reproducibility of PARIETAL : A Deep Learning Brain Extraction Algorithm

2021 ◽  
Author(s):  
Sergi Valverde ◽  
Llucia Coll ◽  
Liliana Valencia ◽  
Albert Clèrigues ◽  
Arnau Oliver ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Extraction ◽  
Extraction Algorithm

scholarly journals Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yoojoong Kim ◽  
Jeong Hyeon Lee ◽  
Sunho Choi ◽  
Jeong Moon Lee ◽  
Jong-Ho Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Language Processing ◽  
Extraction Methods ◽  
Complex Nature ◽  
Keyword Extraction ◽  
Extraction Algorithm ◽  
Pathology Reports ◽  
Natural Language Process ◽  
Present Algorithm

AbstractPathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword extraction for pathology reports is necessary to summarize the informative text and reduce intensive time consumption. In this study, we employed a deep learning model for the natural language process to extract keywords from pathology reports and presented the supervised keyword extraction algorithm. We considered three types of pathological keywords, namely specimen, procedure, and pathology types. We compared the performance of the present algorithm with the conventional keyword extraction methods on the 3115 pathology reports that were manually labeled by professional pathologists. Additionally, we applied the present algorithm to 36,014 unlabeled pathology reports and analysed the extracted keywords with biomedical vocabulary sets. The results demonstrated the suitability of our model for practical application in extracting important data from pathology reports.

scholarly journals 3D U-Net for Skull Stripping in Brain MRI

2019 ◽  
Vol 9 (3) ◽  
pp. 569 ◽  
Author(s):  
Hyunho Hwang ◽  
Hafiz Zia Ur Rehman ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Extraction ◽  
Skull Stripping ◽  
Segmentation Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network ◽  
The Brain

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

scholarly journals A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Inyoung Bae ◽  
Jong-Hee Chae ◽  
Yeji Han
Keyword(s):  
Magnetic Resonance ◽  
3D Imaging ◽  
Brain Images ◽  
Fuzzy C Means ◽  
Brain Extraction ◽  
Imaging Sequence ◽  
Extraction Algorithm ◽  
Infant Brain ◽  
2D And 3D ◽  
The Brain

AbstractIt is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0–24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.

scholarly journals Bridge Extraction Algorithm Based on Deep Learning and High-Resolution Satellite Image

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wenbing Yang ◽  
Xiaoqi Gao ◽  
Chunlei Zhang ◽  
Feng Tong ◽  
Guantian Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Image ◽  
Structural Similarity ◽  
Remote Sensing Images ◽  
The Road ◽  
Extraction Algorithm ◽  
Length Width ◽  
On The Road

This paper proposes a novel method of extracting roads and bridges from high-resolution remote sensing images based on deep learning. Edge detection is performed on the images in the road area along with the road skeleton line, and the result of the detected binary edge is vectorized. The interference of protective belts on both sides of the road, road vehicles, road green belts, traffic signs, etc. and the shadow interference of the bridge itself are eliminated to determine the parallel sides of the road. The bridge features on the road are used to locate the detected bridge and obtain information such as the location, length, width, and direction of the bridge, verifying the experimental results of the Shaoguan Le point images. In addition, in order to learn higher-level road feature information, the algorithm in this paper introduces the hollow convolution and multicore pooling modules. Secondly, the residual refinement network further refines the output of the prediction network to improve the ambiguity of the prediction network results. In addition, in view of the small proportion of road pixels in remote sensing images, the network also integrates binary cross entropy, structural similarity, and intersection ratio loss function to reduce road information loss. The applicability of the proposed study was tested, and the results show that the algorithm is very effective for the extraction of road and bridge targets.

Research and realization of video target detection system based on deep learning

2019 ◽  
Vol 18 (01) ◽  
pp. 1941010 ◽  
Author(s):  
Tao Fan
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Detection System ◽  
Practical Significance ◽  
Classification Problems ◽  
Processing Efficiency ◽  
Extraction Algorithm ◽  
Background Extraction ◽  
Region Extraction ◽  
Image Pixels

The era of big data increases the number and scale of videos day by day, which brings challenges to video target detection. Improving the efficiency and speed of video target detection is of practical significance to image object detection and recognition. Deep learning has been a popular neural network with multilayer structure in recent years. By learning a deep nonlinear network structure, the generalization ability of complex classification problems is improved. In order to achieve higher processing efficiency and accuracy, this topic will study the latest achievements of machine learning method — deep learning — to realize target category detection. In the background extraction step, an improved meaning-based background extraction algorithm and an interest region extraction algorithm to reduce image pixels are proposed. The test results show that the proposed algorithm has good performance of video target detection

Sign in / Sign up

Export Citation Format

Share Document