A Hybrid Technique for Medical Image Segmentation

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.

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MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

BMC Medical Imaging ◽

10.1186/s12880-020-00543-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dominik Müller ◽

Frank Kramer

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Cross Validation ◽

Data Augmentation ◽

Semantic Segmentation ◽

Medical Image Segmentation ◽

Tumor Segmentation ◽

Data Set ◽

Public Data

Abstract Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. Implementation The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Results Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. Conclusions With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.

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Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation

Nordic Machine Intelligence ◽

10.5617/nmi.9126 ◽

2021 ◽

Vol 1 (1) ◽

pp. 20-22

Author(s):

Awadelrahman M. A. Ahmed ◽

Leen A. M. Ali

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Input Image ◽

Medical Image Segmentation ◽

Jaccard Index ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks

This paper contributes in automating medical image segmentation by proposing generative adversarial network based models to segment both polyps and instruments in endoscopy images. A main contribution of this paper is providing explanations for the predictions using layer-wise relevance propagation approach, showing which pixels in the input image are more relevant to the predictions. The models achieved 0.46 and 0.70, on Jaccard index and 0.84 and 0.96 accuracy, on the polyp segmentation and the instrument segmentation, respectively.

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Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2019100103 ◽

2019 ◽

Vol 8 (4) ◽

pp. 39-59

Author(s):

Shashwati Mishra ◽

Mrutyunjaya Panda

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Image Segmentation ◽

Medical Image ◽

Medical Image Segmentation ◽

Hybrid Technique ◽

Membership Functions ◽

Novel Approach ◽

Fitness Value ◽

Sensitivity Specificity

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

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A Review of Deep-Learning-Based Medical Image Segmentation Methods

Sustainability ◽

10.3390/su13031224 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1224

Author(s):

Xiangbin Liu ◽

Liping Song ◽

Shuai Liu ◽

Yudong Zhang

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Rapid Development ◽

Medical Image Segmentation ◽

Deep Convolutional Neural Networks ◽

Biomedical Image Processing ◽

Data Set ◽

Segmentation Methods

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.

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Whale Optimization for Wavelet-Based Unsupervised Medical Image Segmentation: Application to CT and MR Images

International Journal of Computational Intelligence Systems ◽

10.2991/ijcis.d.200625.001 ◽

2020 ◽

Vol 13 (1) ◽

pp. 941

Author(s):

Thavavel Vaiyapuri ◽

Haya Alaskar

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Image Segmentation ◽

Mr Images ◽

Whale Optimization

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Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms ◽

10.4018/978-1-7998-8048-6.ch045 ◽

2021 ◽

pp. 896-916

Author(s):

Shashwati Mishra ◽

Mrutyunjaya Panda

Keyword(s):

Genetic Algorithm ◽

Fuzzy Logic ◽

Image Segmentation ◽

Medical Image ◽

Medical Image Segmentation ◽

Hybrid Technique ◽

Membership Functions ◽

Novel Approach ◽

Sensitivity Specificity ◽

Processing Techniques

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

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EnGAN: Enhancement Generative Adversarial Network in Medical Image Segmentation

10.21203/rs.3.rs-1219874/v1 ◽

2022 ◽

Author(s):

Erqiang Deng ◽

Zhiguang Qin ◽

Dajiang Chen ◽

Zhen Qin ◽

Yi Ding ◽

...

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Image Segmentation ◽

Sample Space ◽

Original Image ◽

Target Domain ◽

Data Set ◽

Image Distribution ◽

Data Imbalance ◽

Space Data

Abstract Deep learning has been widely used in medical image segmentation, although the accuracy is affected by the problems of small sample space, data imbalance, and cross-device differences. Aiming at such issues, a enhancement GAN network is proposed by using the domain transferring of the adversarial generation network to enhance the original medical images. Specifically, based on retaining the transferability of the original GAN network, a new optimizer is added to generate a sample space with a continuous distribution, which can be used as the target domain of the original image transferring. The optimizer back-propagates the labels of the supervised data set through the segmentation network and maps the discrete distribution of the labels to the continuous image distribution, which has a high similarity to the original image but improves the segmentation efficiency.On this basis, the optimized distribution is taken as the target domain, and the generator and discriminator of the GAN network are trained so that the generator can transfer the original image distribution to the target distribution. extensive experiments are conducted based on MRI, CT, and ultrasound data sets. The experimental results show that, the proposed method has a good generalization effect in medical image segmentation, even when the data set has limited sample space and data imbalance to a certain extent.

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