Few-sample Multi-organ Abdominal Image Segmentation with Mean Teacher Model

2020 ◽  
Author(s):  
Pingyi Chen ◽  
Tianyu Chen ◽  
Zhiqiang Yang ◽  
Tian Wang ◽  
Mengyi Zhang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Abdominal Image ◽  
Teacher Model

Abdominal Image Segmentation Using Three-Dimensional Deformable Models

1998 ◽  
Vol 33 (6) ◽  
pp. 348-355 ◽  
Author(s):  
LUOMIN GAO ◽  
DAVID G. HEATH ◽  
ELLIOT K. FISHMAN
Keyword(s):  
Image Segmentation ◽  
Three Dimensional ◽  
Deformable Models ◽  
Abdominal Image

scholarly journals A Generative Adversarial Network Fused with Dual-Attention Mechanism and Its Application in Multitarget Image Fine Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jian Yin ◽  
Zhibo Zhou ◽  
Shaohua Xu ◽  
Ruiping Yang ◽  
Kun Liu
Keyword(s):  
Image Segmentation ◽  
Feature Detection ◽  
Attention Mechanism ◽  
Small Target ◽  
Residual Network ◽  
Generative Adversarial Network ◽  
Abdominal Image ◽  
Feature Enhancement ◽  
Adversarial Network ◽  
Segmentation Boundary

Aiming at the problem of insignificant target morphological features, inaccurate detection and unclear boundary of small-target regions, and multitarget boundary overlap in multitarget complex image segmentation, combining the image segmentation mechanism of generative adversarial network with the feature enhancement method of nonlocal attention, a generative adversarial network fused with attention mechanism (AM-GAN) is proposed. The generative network in the model is composed of residual network and nonlocal attention module, which use the feature extraction and multiscale fusion mechanism of residual network, as well as feature enhancement and global information fusion ability of nonlocal spatial-channel dual attention to enhance the target features in the detection area and improve the continuity and clarity of the segmentation boundary. The adversarial network is composed of fully convolutional networks, which penalizes the loss of information in small-target regions by judging the authenticity of prediction and label segmentation and improves the detection ability of the generative adversarial model for small targets and the accuracy of multitarget segmentation. AM-GAN can use the GAN’s inherent mechanism that reconstruct and repair high-resolution image, as well as the ability of nonlocal attention global receptive field to strengthen detail features, automatically learn to focus on target structures of different shapes and sizes, highlight salient features useful for specific tasks, reduce the loss of image detail features, improve the accuracy of small-target detection, and optimize the segmentation boundary of multitargets. Taking medical MRI abdominal image segmentation as a verification experiment, multitargets such as liver, left/right kidney, and spleen are selected for segmentation and abnormal tissue detection. In the case of small and unbalanced sample datasets, the class pixels’ accuracy reaches 87.37%, the intersection over union is 92.42%, and the average Dice coefficient is 93%. Compared with other methods in the experiment, the segmentation precision and accuracy are greatly improved. It shows that the proposed method has good applicability for solving typical multitarget image segmentation problems such as small-target feature detection, boundary overlap, and offset deformation.

scholarly journals Liver and Tumor Segmentation Techniques for CT Abdominal Images

2019 ◽  
Vol 9 (2S) ◽  
pp. 555-560
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Digital Image ◽  
Automatic Segmentation ◽  
Brightness Contrast ◽  
Tumor Segmentation ◽  
Texture Image ◽  
Abdominal Image ◽  
Liver Image

Image segmentation is one of the important step in digital image processing where the images are partitioned into different segments based on several properties like brightness, contrast, intensity and texture. Image processing includes several steps among which image segmentation is the difficult task. Accurate segmentation is the fundamental step in digital image processing. Segmentation can be performed manually, but as it is a tedious task, automatic segmentation techniques which gives more accuracy has to be found. Many recent segmentation techniques for liver image segmentation are discussed here. Some of the techniques to segment liver from CT scanned abdominal image and to segment tumor from the liver are discussed. The main objective is to highlight various techniques which can aid in developing a novel segmentation technique.

Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation

2022 ◽  
pp. 016173462110698
Author(s):  
Vahid Ashkani Chenarlogh ◽  
Mostafa Ghelich Oghli ◽  
Ali Shabanzadeh ◽  
Nasim Sirjani ◽  
Ardavan Akhavan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Head Circumference ◽  
Ultrasound Image ◽  
Medical Image Segmentation ◽  
Abdominal Circumference ◽  
Grand Challenge ◽  
Fetal Head ◽  
Fetal Ultrasound ◽  
Abdominal Image ◽  
Four Blocks

U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers. There are four blocks in the encoder-decoder path. The results of our proposed architecture were evaluated using a prepared dataset for head circumference and abdominal circumference segmentation tasks, and a public dataset (HC18-Grand challenge dataset) for fetal head circumference measurement. The proposed fast network significantly improved the processing time in comparison with U-Net, dilated U-Net, R2U-Net, attention U-Net, and MFP U-Net. It took 0.47 seconds for segmenting a fetal abdominal image. In addition, over the prepared dataset using the proposed accurate model, Dice and Jaccard coefficients were 97.62% and 95.43% for fetal head segmentation, 95.07%, and 91.99% for fetal abdominal segmentation. Moreover, we have obtained the Dice and Jaccard coefficients of 97.45% and 95.00% using the public HC18-Grand challenge dataset. Based on the obtained results, we have concluded that a fine-tuned and a simple well-structured model used in clinical devices can outperform complex models.

Tripled-Uncertainty Guided Mean Teacher Model for Semi-supervised Medical Image Segmentation

2021 ◽  
pp. 450-460
Author(s):  
Kaiping Wang ◽  
Bo Zhan ◽  
Chen Zu ◽  
Xi Wu ◽  
Jiliu Zhou ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Teacher Model
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