scholarly journals Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation

2018 ◽  
Vol 34 (6-8) ◽  
pp. 1043-1052 ◽  
Author(s):  
Lei Bi ◽  
Dagan Feng ◽  
Jinman Kim
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Convolutional Network ◽  
Adversarial Learning ◽  
Fully Convolutional Network

scholarly journals Medical Image Segmentation based on Fully Convolutional Network and Minimizing Energy Between Curves

TEM Journal ◽  
2020 ◽  
pp. 1348-1356
Author(s):  
Vo Thi Hong Tuyet ◽  
Nguyen Thanh Binh
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Contour Detection ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
High Quality ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Object Contour

Energy between curves of image has useful for object contour. The edge map is an important task for recognition. The shape that is found by linking between edges will clearly present the useful information of objects. The aim of medical image segmentation is the representation of a medical image into small pieces. In this process, feature extraction must adapt with edge map completely. This paper proposed a solution for medical image segmentation based on fully convolutional network with gradient vector flow snake in bandelet domain. Our approach depends on decomposition in bandelet domain and reconstruction in contour detection by fully convolutional network combining with gradient vector flow snake. To improve the accuracy of the feature's extraction processing, the proposed method detected the edge map in bandelet domain by using fully convolutional network. And its reconstructed objects contour by using gradient vector flow snake combined with the boundary condition. The results of the proposed method have the segmentation clearly with small details of medical images in high-quality and low-quality cases.

Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation

2020 ◽  
pp. 12-30
Author(s):  
V. R. S. Mani
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image Segmentation ◽  
Learning Models ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Comprehensive Picture ◽  
Application Methods ◽  
Segmentation Task

In this chapter, the author paints a comprehensive picture of different deep learning models used in different multi-modal image segmentation tasks. This chapter is an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different types of multi-modal images and the corresponding types of convolution neural networks used in the segmentation task. The chapter starts with an introduction to CNN topology and describes various models like Hyper Dense Net, Organ Attention Net, UNet, VNet, Dilated Fully Convolutional Network, Transfer Learning, etc.

scholarly journals Spine Medical Image Segmentation Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qingfeng Zhang ◽  
Yun Du ◽  
Zhiqiang Wei ◽  
Hengping Liu ◽  
Xiaoxia Yang ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Processing Time ◽  
Learning Algorithm ◽  
Medical Image Segmentation ◽  
Clinical Value ◽  
Convolutional Network ◽  
Spinal Mri

The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5–10 s, and the processing time is significantly shortened ( P < 0.05 ). The Acc, Sen, and Spe results of BN-U-Net segmentation algorithm were 94.54 ± 3.56%, 88.76 ± 2.67%, and 86.27 ± 6.23%, respectively, which were significantly improved compared with FCN algorithm and U-Net algorithm ( P < 0.05 ). In summary, the improved U-Net network algorithm used in this study significantly improves the quality of spinal MRI images by automatic segmentation of MRI images, which is worthy of further promotion in the field of spinal medical image segmentation.

scholarly journals Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation

2019 ◽  
Vol 33 ◽  
pp. 1085-1092 ◽  
Author(s):  
Dong Nie ◽  
Li Wang ◽  
Lei Xiang ◽  
Sihang Zhou ◽  
Ehsan Adeli ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Structural Information ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Adversarial Learning ◽  
Image Guided Radiation Therapy ◽  
Adversarial Network ◽  
Specific Effects ◽  
Therapy And Diagnosis

Medical image segmentation is a key step for various applications, such as image-guided radiation therapy and diagnosis. Recently, deep neural networks provided promising solutions for automatic image segmentation; however, they often perform good on regular samples (i.e., easy-to-segment samples), since the datasets are dominated by easy and regular samples. For medical images, due to huge inter-subject variations or disease-specific effects on subjects, there exist several difficult-to-segment cases that are often overlooked by the previous works. To address this challenge, we propose a difficulty-aware deep segmentation network with confidence learning for end-to-end segmentation. The proposed framework has two main contributions: 1) Besides the segmentation network, we also propose a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation network. We relax the adversarial learning to confidence learning by decreasing the priority of adversarial learning, so that we can avoid the training imbalance between generator and discriminator. 2) We propose a difficulty-aware attention mechanism to properly handle hard samples or hard regions considering structural information, which may go beyond the shortcomings of focal loss. We further propose a fusion module to selectively fuse the concatenated feature maps in encoder-decoder architectures. Experimental results on clinical and challenge datasets show that our proposed network can achieve state-of-the-art segmentation accuracy. Further analysis also indicates that each individual component of our proposed network contributes to the overall performance improvement.

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation
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