Spine Medical Image Segmentation Based on Deep Learning

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.

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Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Entropy ◽

10.3390/e22080844 ◽

2020 ◽

Vol 22 (8) ◽

pp. 844

Author(s):

Baixin Jin ◽

Pingping Liu ◽

Peng Wang ◽

Lida Shi ◽

Jing Zhao

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Deep Learning ◽

Optic Disc ◽

Medical Image ◽

Medical Image Analysis ◽

Medical Image Segmentation ◽

Attention Mechanism ◽

Cross Entropy ◽

Segmentation Task

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.

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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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Efficient Medical Image Segmentation Of COVID-19 Chest CT Images Based on Deep Learning Techniques

2021 International Conference on Emerging Smart Computing and Informatics (ESCI) ◽

10.1109/esci50559.2021.9397043 ◽

2021 ◽

Author(s):

Sanika Walvekar ◽

Swati Shinde

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Ct Images ◽

Medical Image Segmentation ◽

Chest Ct ◽

Learning Techniques

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HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

Symmetry ◽

10.3390/sym13112107 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2107

Author(s):

Xin Wei ◽

Huan Wan ◽

Fanghua Ye ◽

Weidong Min

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Loss Function ◽

Network Structure ◽

Medical Diagnosis ◽

Medical Image ◽

Medical Image Segmentation ◽

Training Mode ◽

Cooperative Training

In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.

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Region-to-boundary deep learning model with multi-scale feature fusion for medical image segmentation

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.103165 ◽

2022 ◽

Vol 71 ◽

pp. 103165

Author(s):

Xiaowei Liu ◽

Lei Yang ◽

Jianguo Chen ◽

Siyang Yu ◽

Keqin Li

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Feature Fusion ◽

Learning Model ◽

Medical Image Segmentation ◽

Scale Feature ◽

Multi Scale ◽

Deep Learning Model

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Ensemble-based deep meta learning for medical image segmentation

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219221 ◽

2021 ◽

pp. 1-7

Author(s):

Usman Ahmed ◽

Jerry Chun-Wei Lin ◽

Gautam Srivastava

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Learning Algorithm ◽

State Of The Art ◽

Medical Image Segmentation ◽

The State ◽

Data Set ◽

Deep Learning Algorithm ◽

Meta Learning ◽

Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

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