Technical Note: The effect of image annotation with minimal manual interaction for semi‐automatic prostate segmentation in CT images using fully convolutional neural networks

2021 ◽  
Author(s):  
Maysam Shahedi ◽  
James D. Dormer ◽  
Martin Halicek ◽  
Baowei Fei
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Ct Images ◽  
Technical Note ◽  
Prostate Segmentation ◽  
Fully Convolutional Neural Networks ◽  
Manual Interaction

scholarly journals Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 5472-5474
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Convolutional Neural Networks ◽  
Proposal A ◽  
Fully Automatic ◽  
Lung Ct

Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area

scholarly journals Deep learning models for COVID-19 infected area segmentation in CT images

2020 ◽  
Author(s):  
Athanasios Voulodimos ◽  
Eftychios Protopapadakis ◽  
Iason Katsamenis ◽  
Anastasios Doulamis ◽  
Nikolaos Doulamis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Learning Models ◽  
Fully Convolutional Neural Networks ◽  
Infected Area ◽  
Ct Data

Recent studies indicated that detecting radiographic patterns on CT chest scans could in some cases yield higher sensitivity and specificity for COVID-19 detection compared to other methods such as RTPCR. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images.

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

scholarly journals Fully convolutional neural networks for polyp segmentation in colonoscopy

2017 ◽  
Author(s):  
Patrick Brandao ◽  
Evangelos Mazomenos ◽  
Gastone Ciuti ◽  
Renato Caliò ◽  
Federico Bianchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fully Convolutional Neural Networks
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