Table 1 Summary of the state-of-the-art techniques in medical image
From: Learning to detect boundary information for brain image segmentation
Publication | Method | Purpose |
|---|---|---|
Guoqiang et al. [23] | GVF | Segmentation of brain MRI image with GVF snake model |
Lei et al. [24] | Clustering method | MR brain image segmentation |
Somasundaram et al. [25] | Intensity thresholding | Brain portion segmentation from MRI |
Jiao et al. [26] | \(MI-GAN\) | Brain image segmentation based on bilateral symmetry information |
Jimenez et al. [27] | 3DCycleGAN | Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information |
Tan Ou et al. [28] | Atlas | Automatic segmentation of human brain images |
Snell et al. [29] | Active surfaces | Model-based segmentation of the brain from 3-D MR |
Lei et al. [24] | Clustering method | MR brain image segmentation |
Yao et al. [30] | Adjustable method | High effective medical image segmentation |
Zhang et al. [31] | Active volume model with shape priors | 3D segmentation of rodent brain structures |
Liya et al. [32] | Object detection | Feature extraction and morphological operations |
Mallick et al. [33] | Intelligent technique | CT brain image segmentation |
Zhou et al. [34] | Encoder–decoder networks | Low-contrast medical image segmentation |
Qu et al. [35] | FCD detection | Estimating blur at the brain gray-white matter boundary |
Shen et al. [36] | Fully convolutional networks | Neuronal boundary detection |
Chakraborty et al. [37] | An integrated approach | Boundary finding in medical images |
Khaled et al. [17] | 3D, FCNÂ +Â MILÂ +Â GÂ +Â K | Brain tissues segmentation |
Khaled et al. [38] | Multi-stage GAN | Brain tissues segmentation |