Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Huang, Yaqi; Ge, Heng; Ji, Changjin; Xiong, Hongwei

doi:10.1038/s41467-020-19392-7

Cited by 11 publications

(6 citation statements)

References 52 publications

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“…Retinal vascular imaging can provide clinical prognostic information for patients suffering from specific cardiovascular and ophthalmic diseases [1]. Segmentation of the retinal vasculature is a prerequisite for monitoring the status of the retinal vascular network [2]. Currently, retinal vascular segmentation is highly dependent on manual work by experienced ophthalmologists, which is tedious, time-consuming, and has low reproducibility.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

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Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

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Section: Introductionmentioning

confidence: 99%

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Zhang

Zhang²,

Feng

2021

Journal of Healthcare Engineering

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“…The accuracy and resolution requirements of the image are extremely high. With the rapid development of artificial intelligence and informatization, the application of deep learning in medical imaging has made great contributions to the segmentation and diagnosis of medical images [ 15 ]. In this research, the U -Net algorithm was improved and compared with the CNN algorithm and the U -Net algorithm.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation

Zhou

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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To study the effect of computerized tomography (CT) images based on deep learning algorithms on the diagnosis of pulmonary nodules and the effect of radiofrequency ablation (RFA), the U-shaped fully convolutional neural network (FCNN) (U-Net) was enhanced. The convolutional neural network (CNN) algorithm was compared with the U-Net algorithm, and segmentation performances were analyzed. Then, it was applied to the CT image diagnosis of 110 lung cancer patients admitted to hospital. The patients in the observation group (55 cases) were diagnosed based on the improved U-Net algorithm, while those in the control group (55 cases) were diagnosed by traditional methods and then treated with RFA. The Dice coefficient (0.8753) and intersection over union (IOU) (0.8788) obtained by the proposed algorithm were remarkably higher than the Dice coefficient (0.7212) and IOU (0.7231) obtained by the CNN algorithm, and the differences were considerable ( P < 0.05 ). The boundary of the pulmonary nodule can be segmented more accurately by the proposed algorithm, which had the segmentation result closest to the gold standard among the three algorithms. The diagnostic accuracy of the pulmonary nodule in the observation group (95.3%) was superior to that of the control group (90.7%). The long diameter, volume, and maximum area of the pulmonary nodule of the observation group were significantly higher than those of the control group, with substantial differences ( P < 0.05 ). Patients were reexamined after one, three, and six months of treatment, and 71 patients (64.55%) had complete remission, 32 patients (29.10%) had partial remission, 6 patients (5.45%) had stable disease, and 1 patient (0.90%) had disease progression. The remission rate (complete remission + partial remission) was 93.65%. The improved U-NET algorithm had good image segmentation performance and ideal segmentation effect. It can clearly display the shape of pulmonary nodules, locate the lesions, and accurately evaluate the therapeutic effect of RFA, which had clinical application value.

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“…Medical image segmentation techniques can be divided into two categories: traditional segmentation techniques and deep learning-based methods. e former includes thresholdbased segmentation [8], edge-based segmentation [9], regionbased segmentation [10,11], and active contour model-based techniques [12,13], and the latter is mainly neural networkbased segmentation [14][15][16][17][18]. Earlier studies have been applied to the lumbar spine segmentation by traditional segmentation techniques.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net

Wang

Jiang

Yang

et al. 2022

Computational Intelligence and Neuroscience

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Lumbar spine segmentation is important to help doctors diagnose lumbar disc herniation (LDH) and patients’ rehabilitation treatment. In order to accurately segment the lumbar spine, a lumbar spine image segmentation algorithm based on improved Attention U-Net is proposed. The algorithm is based on Attention U-Net, the attention module based on multilevel feature map fusion is adopted, two residual modules are introduced instead of the original convolution blocks. a hybrid loss function is used for prediction during the training process, and finally, the image superposition process is realized. In this experiment, we expanded 420 lumbar MRI images of 180 patients to 1000 images and trained them by different algorithms, respectively, and accuracy, recall, and Dice similarity coefficient metrics were used to analyze these algorithms. The results show that compared with SVM, FCN, R-CNN, U-Net, and Attention U-Net models, the improved model achieved better results in all three evaluations, with 95.50%, 94.53%, and 95.01%, respectively, which proves the better performance of the proposed method for segmentation in lumbar disc and caudal vertebrae.

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Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Cited by 11 publications

References 52 publications

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

Deep Learning-Based CT Imaging in the Diagnosis of Treatment Effect of Pulmonary Nodules and Radiofrequency Ablation

Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net

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