Zhu et al., 2022 - Google Patents
Attention-Unet: A deep learning approach for fast and accurate segmentation in medical imagingZhu et al., 2022
View PDF- Document ID
- 1016650914632526757
- Author
- Zhu Z
- Yan Y
- Xu R
- Zi Y
- Wang J
- Publication year
- Publication venue
- Journal of Computer Science and Software Applications
External Links
Snippet
Accurate extraction of the bronchial tubes from lung computed tomography (CT) images is crucial for evaluating respiratory function and diagnosing diseases. Current bronchial segmentation methods often rely heavily on substantial human-computer interaction to …
- 230000011218 segmentation 0 title abstract description 65
Classifications
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- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
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- G—PHYSICS
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
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- G06T2207/30048—Heart; Cardiac
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- G06T2207/10104—Positron emission tomography [PET]
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
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- G06—COMPUTING; CALCULATING; COUNTING
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- G06T7/0014—Biomedical image inspection using an image reference approach
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
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- G06T2207/30172—Centreline of tubular or elongated structure
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- G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
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