Image super-resolution (SR) provides an effective alternative to enhance the resolution of MR images from the perspective of postprocessing (Zhao et al., 2018c), which aims at recovering a HR image from one or more low-resolution (LR) images. As a postprocessing method, image SR is an ...
emphasize on image segmentation, which defines the set of segments that collectively covers an entire given single image, or a set of image contours, such as These latter tissues are extracted from MRI images in order to detect a Total Intracranial Volume (TIV) and a Whole Brain Volume (WBV...
aTypically, an MRI delivers an image of the brain case, with a resolution of approximately 1 cubic millimeter. Since the brain is about one cubic decimeter, our tumor image sits inside a 100 x 100 x 100 voxel space, where each voxel represents 1mm3. We assume that the shape given by ...
Multi-level feature extraction and reconstruction for 3D MRI image super-resolution 2024, Computers in Biology and Medicine Citation Excerpt : Pham et al. [22] utilized a very deep model with residual learning and a large receptive field to SR reconstruct MRI images. Wang et al. [23] combined...
摘要: early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of...关键词:Brain cancer diagnosis Deep learning EfficientNetv2 ECA GAM Grad-CAM ...
To produce precise automated segmentation, high-resolution features (image properties that the CNN learns from the training set) from the contracting path were concatenated with the up-sampled feature maps. Feature maps were injected into the deeper layers of the network to mitigate the problem of ...
Not only will this increase the resolution of the resulting segmentation, it will also allow the method to take advantage of the locality assumption, which posits that small groups of adjacent pixels are more likely to belong to the same class. Third, ...
DASample is a technique used in the Edge Path to enhance the receptive field and capture fine edge details utilizes dilated convolutions to expand the network’s effective receptive field while retaining spatial resolution. The DASample operation applies dilated convolutions with thoughtfully selected dil...
This paper proposes an accurate MRI brain tumor segmentation based on a Rotating Triangular Section with Fuzzy C-Means Optimization. Magnetic Resonance Imaging has become so popular due to its capability to differentiate tumors from the non-tumor region. The proposed method initially eliminates most ...
ProjectPathologyResolution (isotropic)b-ValuesPreprocessingScanner field strength TRACEDa healthy 2.5 mm 3x b = 0 mm/s2 denoising, eddy current and motion correction 3T 20x b = 1000 mm/s2 48x b = 2000 mm/s2 64x b = 3000 mm/s2 Internal (Healthy) healthy 2.5...