Multi-task relationship learning method for centrum positioning, identification and segmentation in nuclear magnetic resonance imaging

Abstract

The invention relates to a multi-task relationship learning method for centrum positioning, identification and segmentation in nuclear magnetic resonance imaging. According to the method, a relationship among multiple tasks is fully utilized based on deep learning, and challenges caused by intervertebral similarity and image quality are greatly improved. For automatic analysis of the spine, an effective multi-task learning framework is provided. The framework can be easily popularized to the application of other images, and a universal framework is provided for effectively solving the three tasks of image positioning, identification and segmentation.

Description

technical field

[0001] The invention relates to a multi-task relational learning method for vertebral body positioning, recognition and segmentation in nuclear magnetic resonance imaging, and belongs to the technical field of medical image processing. Background technique

[0002] In the context of computer-assisted spinal surgery, it is important to know exactly the shape of individual vertebral bodies, e.g. for spinal biopsy, implant or pedicle screw insertion, etc. But in most cases, it is not only required to obtain the shape of the vertebral body through accurate segmentation, but also to locate and identify the vertebral body. Automatic segmentation, localization, and labeling of vertebral bodies in computed tomography (CT) or magnetic resonance imaging (MRI) spine imaging has become an important tool for clinical tasks, including pathological diagnosis, surgical planning, and postoperative evaluation. Specific applications such as fracture detection, tumor detection....

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