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Mar 11, 2021 · The MRI scan of the brain provides a 3D image of the brain scanned in x, y, z space at an appropriate slice of thickness usually ranging from 1 to 2 mm (e.g., a slice thickness of 1 mm × 1 mm × 1 mm is considered quite good). The slice thickness need not be isometric and will depend upon the MR scanner, gradient coil, channels, scan time ...
Mar 20, 2020 · With the development of high-resolution micro-optical imaging, whole-brain images can be acquired at the cellular level. However, brain regions in microscopic images are aggregated by discrete neurons with blurry boundaries, the complex and variable features of brain regions make it challenging to accurately segment brain regions.
- Chaozhen Tan, Yue Guan, Zhao Feng, Hong Ni, Zoutao Zhang, Zhiguang Wang, Xiangning Li, Jing Yuan, Hu...
- 2020
Aug 10, 2021 · In general, brain image segmentation methods are categorized as intensity-based, machine learning and hybrid, as summarized in Table 3. These approaches have both collective and progressive manners. The collective aim is to segment: (i) healthy brain tissues, (ii) brain sub-structures, and (iii) tumor and intra-tumor regions.
- Ali Fawzi, Anusha Achuthan, Bahari Belaton
- 10.3390/brainsci11081055
- 2021
- Brain Sci. 2021 Aug; 11(8): 1055.
- The framework for DeepBrainSeg. (A) Network training, the acquisition of images and labels, samples extraction, building and training the CNN.
- The architecture of dual-pathway CNN. The network consists of dual pathways that take the smaller and larger patch as input, respectively. Each pathway has three hidden layer which have the main components of a convolutional layer, a ReLU layer, an LRN layer, and a pooling layer.
- Comparison of the segmentation effect with and without localization. (A) A superposition of the original image and the manually segmented lines.
- Performance of different patch size. (A) Box plots showing the Dice ratio for five different patch sizes at a single scale. (B) Box plots showing the Dice ratio for the larger patch at different multiples of the smaller patch size.
Oct 1, 2021 · Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes ...
- Yeshu Li, Jonathan Cui, Yilun Sheng, Yilun Sheng, Xiao Liang, Jingdong Wang, Eric I.-Chao Chang, Yan...
- 2021
Oct 13, 2021 · High content imaging of the brain holds the promise of improving our understanding of the brain’s circuitry. Here, the authors present a tool that automates the scaling and segmentation of ...
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Apr 19, 2023 · We show also how brain extraction, as a preliminary step, can help to segment brain tissues with a K-Means clustering algorithm. For many neuroscience applications, brain extraction in MRI images ...
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