scholarly journals Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network

2021 ◽  
Author(s):  
Afifa Khaled ◽  
Jian-Jun Han
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Adult Brain ◽  
Stage Model ◽  
Brain Segmentation ◽  
One Stage ◽  
Multi Stage ◽  
Wide Range ◽  
Brain Tissues

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

scholarly journals Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network

2021 ◽  
Author(s):  
Afifa Khaled ◽  
Jian-Jun Han
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Adult Brain ◽  
Stage Model ◽  
Brain Segmentation ◽  
One Stage ◽  
Multi Stage ◽  
Wide Range ◽  
Brain Tissues

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

scholarly journals Annotation-efficient deep learning for automatic medical image segmentation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shanshan Wang ◽  
Cheng Li ◽  
Rongpin Wang ◽  
Zaiyi Liu ◽  
Meiyun Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
High Performance ◽  
Critical Role ◽  
Real Life ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Wide Range ◽  
Open Datasets

AbstractAutomatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets. Methodological analyses and empirical evaluations are conducted, and we demonstrate that AIDE surpasses conventional fully-supervised models by presenting better performance on open datasets possessing scarce or noisy annotations. We further test AIDE in a real-life case study for breast tumor segmentation. Three datasets containing 11,852 breast images from three medical centers are employed, and AIDE, utilizing 10% training annotations, consistently produces segmentation maps comparable to those generated by fully-supervised counterparts or provided by independent radiologists. The 10-fold enhanced efficiency in utilizing expert labels has the potential to promote a wide range of biomedical applications.

Image Segmentation and the Projections of Graphic Centred Approaches in Medical Image Processing

2020 ◽  
pp. 74-81
Author(s):  
Anandakumar Haldorai ◽  
Shrinand Anandakumar
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Real Life ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Regions Of Interest ◽  
Object Categorization ◽  
Range Image ◽  
Image Evaluation ◽  
Wide Range

– Medical image segmentation is considered the most precarious element in the analysis and processing of real-life images in the clinical sector. Actually, segmentation effects affect the subsequent procedures of image evaluation, life object illustration and description, feature dimensions and the subsequent considerable task levels such as object categorization. In that case, medical image segmentation is the most essential and crucial aspect for aiding the visualization, delineation and depiction of regions of interest for any particular image. The aspect of physical segmentation of a picture is not just challenging and time-consuming task to do, but also problematic to assure accuracy considering the wide-range image modalities and unguided image quantities which have to be observed. In that case, it now becomes fundamental to evaluate the present approaches of image segmentation based on the application of computing algorithms which require the interaction of users with evaluating medical images. In the process of image segmentation, an anatomic classification requires to be extracted or defined to be projected effectively and independently. In that case, this contribution is focused on image segmentation to extract details for decision-making in the clinical sector. The paper presents generalized and relative techniques that have been categorized into three groups: pixel-centre, edge-centred and region-centred techniques. The paper also provides a highlight of the strengths and weaknesses of these techniques in reference to the appropriateness of a wide-range application in medical image segmentation.

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

Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation

2021 ◽  
Author(s):  
Dachuan Shi ◽  
Ruiyang Liu ◽  
Linmi Tao ◽  
Zuoxiang He ◽  
Li Huo
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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