CN110337670A - 一种基于磁共振图像的脑龄测试方法及脑龄测试装置 - Google Patents
一种基于磁共振图像的脑龄测试方法及脑龄测试装置 Download PDFInfo
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- CN110337670A CN110337670A CN201880000008.4A CN201880000008A CN110337670A CN 110337670 A CN110337670 A CN 110337670A CN 201880000008 A CN201880000008 A CN 201880000008A CN 110337670 A CN110337670 A CN 110337670A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/143—Segmentation; Edge detection involving probabilistic approaches, e.g. Markov random field [MRF] modelling
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Abstract
一种基于磁共振图像的脑龄测试方法、基于磁共振图像的脑龄测试装置、电子设备及计算机可读存储介质。其中,所述基于磁共振图像的脑龄测试方法,包括:获取待测个体的T1加权大脑磁共振图像(S101);基于所述T1加权大脑磁共振图像确定所述待测个体的脑结构、脑叶以及脑组织分割图谱(S102);计算所述脑结构的归一化体积以及所述脑叶的脑萎缩值(S103);将所述归一化体积以及所述脑萎缩值输入脑龄估计模型,获取所述待测个体的脑龄(S104)。该方法可以估算脑龄,方便人们对大脑健康提前进行干预,提高人们对大脑健康的意识。
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PCT国内申请,说明书已公开。
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- PCT国内申请,权利要求书已公开。
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Cited By (2)
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CN113221927A (zh) * | 2020-01-21 | 2021-08-06 | 宏碁股份有限公司 | 模型训练方法与电子装置 |
CN117036793A (zh) * | 2023-07-31 | 2023-11-10 | 复旦大学 | 一种基于pet影像多尺度特征的脑龄评估方法及装置 |
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TWI726574B (zh) | 2020-01-10 | 2021-05-01 | 宏碁股份有限公司 | 模型訓練方法與電子裝置 |
CN111134677B (zh) * | 2020-02-14 | 2021-05-04 | 北京航空航天大学 | 一种基于磁共振影像的卒中致残预测方法及系统 |
CN111415361B (zh) * | 2020-03-31 | 2021-01-19 | 浙江大学 | 基于深度学习的胎儿大脑脑龄估计和异常检测方法及装置 |
KR20230168507A (ko) * | 2022-06-07 | 2023-12-14 | 가천대학교 산학협력단 | 자기공명영상의 뇌 분할 시스템 및 방법 |
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Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113221927A (zh) * | 2020-01-21 | 2021-08-06 | 宏碁股份有限公司 | 模型训练方法与电子装置 |
CN113221927B (zh) * | 2020-01-21 | 2024-01-23 | 宏碁股份有限公司 | 模型训练方法与电子装置 |
CN117036793A (zh) * | 2023-07-31 | 2023-11-10 | 复旦大学 | 一种基于pet影像多尺度特征的脑龄评估方法及装置 |
CN117036793B (zh) * | 2023-07-31 | 2024-04-19 | 复旦大学 | 一种基于pet影像多尺度特征的脑龄评估方法及装置 |
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