EnGAN: Enhancement Generative Adversarial Network in Medical Image Segmentation

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

Electronics ◽

10.3390/electronics10030348 ◽

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.

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Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.3301865 ◽

2019 ◽

Vol 33 ◽

pp. 865-872 ◽

Cited By ~ 27

Author(s):

Cheng Chen ◽

Qi Dou ◽

Hao Chen ◽

Jing Qin ◽

Pheng-Ann Heng

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Domain Adaptation ◽

State Of The Art ◽

Medical Image Segmentation ◽

Target Domain ◽

Learning Procedure ◽

The Neural Networks ◽

Feature Adaptation ◽

Segmentation Task

This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of crossmodality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.

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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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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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Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

10.1007/978-3-030-87199-4_14 ◽

2021 ◽

pp. 149-159

Author(s):

Chen Chen ◽

Kerstin Hammernik ◽

Cheng Ouyang ◽

Chen Qin ◽

Wenjia Bai ◽

...

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Data Augmentation ◽

Medical Image Segmentation ◽

Latent Space ◽

Cooperative Training ◽

Space Data

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Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/455 ◽

2020 ◽

Author(s):

Danbing Zou ◽

Qikui Zhu ◽

Pingkun Yan

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Network Performance ◽

Domain Adaptation ◽

Medical Image Segmentation ◽

Training Data ◽

Tumor Segmentation ◽

Target Domain ◽

Brain Tumor Segmentation ◽

Unsupervised Domain Adaptation

Domain adaptation aims to alleviate the problem of retraining a pre-trained model when applying it to a different domain, which requires large amount of additional training data of the target domain. Such an objective is usually achieved by establishing connections between the source domain labels and target domain data. However, this imbalanced source-to-target one way pass may not eliminate the domain gap, which limits the performance of the pre-trained model. In this paper, we propose an innovative Dual-Scheme Fusion Network (DSFN) for unsupervised domain adaptation. By building both source-to-target and target-to-source connections, this balanced joint information flow helps reduce the domain gap to further improve the network performance. The mechanism is further applied to the inference stage, where both the original input target image and the generated source images are segmented with the proposed joint network. The results are fused to obtain more robust segmentation. Extensive experiments of unsupervised cross-modality medical image segmentation are conducted on two tasks -- brain tumor segmentation and cardiac structures segmentation. The experimental results show that our method achieved significant performance improvement over other state-of-the-art domain adaptation methods.

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A Hybrid Technique for Medical Image Segmentation

Journal of Biomedicine and Biotechnology ◽

10.1155/2012/830252 ◽

2012 ◽

Vol 2012 ◽

pp. 1-7 ◽

Cited By ~ 27

Author(s):

Alamgir Nyma ◽

Myeongsu Kang ◽

Yung-Keun Kwon ◽

Cheol-Hong Kim ◽

Jong-Myon Kim

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Impulsive Noise ◽

Median Filter ◽

Input Image ◽

Medical Image Segmentation ◽

Hybrid Technique ◽

Mr Images ◽

Data Set ◽

Brain Mr Images

Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

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