scholarly journals Lung CT Image Segmentation Using Deep Neural Networks

2018 ◽  
Vol 127 ◽  
pp. 109-113 ◽  
Author(s):  
Brahim Ait Skourt ◽  
Abdelhamid El Hassani ◽  
Aicha Majda
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Deep Neural Networks ◽  
Ct Image ◽  
Lung Ct

scholarly journals ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Great Success ◽  
Morphological Operations ◽  
Ct Image ◽  
Feature Maps ◽  
Learning Framework ◽  
Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

Automated vessel segmentation in lung CT and CTA images via deep neural networks

2021 ◽  
pp. 1-15
Author(s):  
Wenjun Tan ◽  
Luyu Zhou ◽  
Xiaoshuo Li ◽  
Xiaoyu Yang ◽  
Yufei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Vessel Segmentation ◽  
Multi Scale ◽  
Vascular Segmentation ◽  
Study Results ◽  
Lung Ct

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

scholarly journals TOWARDS DISTILLATION OF DEEP NEURAL NETWORKS FOR SATELLITE ON-BOARD IMAGE SEGMENTATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1553-1559
Author(s):  
F. de Vieilleville ◽  
A. Lagrange ◽  
R. Ruiloba ◽  
S. May
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Data Analysis ◽  
Deep Neural Networks ◽  
Distillation Method ◽  
Ship Detection ◽  
Promising Solution

Abstract. Cubesats platforms expansion increases the need to simplify payloads and to optimize downlink data capabilities. A promising solution is to enhance on-board software, in order to take early decisions, automatically. However, the most efficient methods for data analysis are generally large deep neural networks (DNN) oversized to be loaded and processed on limited hardware capacities of cubesats. To use them, we must reduce the size of DNN while accommodating efficiency in terms of both accuracy and inference cost. In this paper, we propose a distillation method which reduces image segmentation deep neural network’s size to fit into on board processors. This method is presented through a ship detection example comparing accuracy and inference costs for several networks.

Lung CT Image Segmentation Based on Mixture Active Contour Model

2014 ◽  
Vol 618 ◽  
pp. 405-409 ◽  
Author(s):  
Ke Qin Tao ◽  
Zhe Qu ◽  
Dong Dong Wang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Learning Algorithm ◽  
Active Contour Model ◽  
Gaussian Mixture ◽  
Ct Image ◽  
Penalty Term ◽  
Level Set Function ◽  
Contour Model ◽  
Lung Ct

In order to solve the difficult problem in lung CT image segmentation, the segmentation method based on Mixture Active Contour Model is proposed and the learning algorithm is presented. It gets the prior information of lung CT image segmentation through Gaussian Mixture Model, couples the penalty term and edge detection of the level set function. Experimental results illustrate the effectiveness of the method based on MACM in solving lung CT image segmentation.

scholarly journals Generative Adversarial Networks for Pre-training of Medical Image Segmentation Networks

2020 ◽  
Author(s):  
Kun Chen ◽  
Manning Wang ◽  
Zhijian Song
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Synthetic Images

Abstract Background: Deep neural networks have been widely used in medical image segmentation and have achieved state-of-the-art performance in many tasks. However, different from the segmentation of natural images or video frames, the manual segmentation of anatomical structures in medical images needs high expertise so the scale of labeled training data is very small, which is a major obstacle for the improvement of deep neural networks performance in medical image segmentation. Methods: In this paper, we proposed a new end-to-end generation-segmentation framework by integrating Generative Adversarial Networks (GAN) and a segmentation network and train them simultaneously. The novelty is that during the training of the GAN, the intermediate synthetic images generated by the generator of the GAN are used to pre-train the segmentation network. As the advances of the training of the GAN, the synthetic images evolve gradually from being very coarse to containing more realistic textures, and these images help train the segmentation network gradually. After the training of GAN, the segmentation network is then fine-tuned by training with the real labeled images. Results: We evaluated the proposed framework on four different datasets, including 2D cardiac dataset and lung dataset, 3D prostate dataset and liver dataset. Compared with original U-net and CE-Net, our framework can achieve better segmentation performance. Our framework also can get better segmentation results than U-net on small datasets. In addition, our framework is more effective than the usual data augmentation methods. Conclusions: The proposed framework can be used as a pre-train method of segmentation network, which helps to get a better segmentation result. Our method can solve the shortcomings of current data augmentation methods to some extent.

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