CN110363218A - 一种胚胎无创评估方法及装置 - Google Patents
一种胚胎无创评估方法及装置 Download PDFInfo
- Publication number
- CN110363218A CN110363218A CN201910491782.6A CN201910491782A CN110363218A CN 110363218 A CN110363218 A CN 110363218A CN 201910491782 A CN201910491782 A CN 201910491782A CN 110363218 A CN110363218 A CN 110363218A
- Authority
- CN
- China
- Prior art keywords
- protokaryon
- embryo
- parameter
- obtains
- development
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 210000001161 mammalian embryo Anatomy 0.000 title claims abstract description 79
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000011161 development Methods 0.000 claims abstract description 39
- 230000018109 developmental process Effects 0.000 claims abstract description 39
- 108010077544 Chromatin Proteins 0.000 claims abstract description 22
- 210000003483 chromatin Anatomy 0.000 claims abstract description 22
- 230000013020 embryo development Effects 0.000 claims abstract description 22
- 238000007689 inspection Methods 0.000 claims abstract description 5
- 235000013601 eggs Nutrition 0.000 claims description 19
- 210000000349 chromosome Anatomy 0.000 claims description 18
- 238000003776 cleavage reaction Methods 0.000 claims description 16
- 230000007017 scission Effects 0.000 claims description 16
- 239000000284 extract Substances 0.000 claims description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 10
- 230000000306 recurrent effect Effects 0.000 claims description 10
- 230000003287 optical effect Effects 0.000 claims description 8
- 230000009087 cell motility Effects 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 6
- 230000001629 suppression Effects 0.000 claims description 5
- 238000013459 approach Methods 0.000 claims description 3
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000012821 model calculation Methods 0.000 claims description 3
- 239000007787 solid Substances 0.000 claims description 2
- 230000001537 neural effect Effects 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000001303 quality assessment method Methods 0.000 abstract description 3
- 210000002257 embryonic structure Anatomy 0.000 abstract description 2
- 238000013527 convolutional neural network Methods 0.000 description 8
- 238000001514 detection method Methods 0.000 description 8
- 238000000605 extraction Methods 0.000 description 7
- 238000000338 in vitro Methods 0.000 description 6
- 239000011159 matrix material Substances 0.000 description 6
- 230000033001 locomotion Effects 0.000 description 5
- 210000001109 blastomere Anatomy 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- 230000000642 iatrogenic effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000004720 fertilization Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000011176 pooling Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000012897 Levenberg–Marquardt algorithm Methods 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- HUTDUHSNJYTCAR-UHFFFAOYSA-N ancymidol Chemical compound C1=CC(OC)=CC=C1C(O)(C=1C=NC=NC=1)C1CC1 HUTDUHSNJYTCAR-UHFFFAOYSA-N 0.000 description 1
- 208000036878 aneuploidy Diseases 0.000 description 1
- 231100001075 aneuploidy Toxicity 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000003416 augmentation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013529 biological neural network Methods 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 210000000805 cytoplasm Anatomy 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000004043 dyeing Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 239000003163 gonadal steroid hormone Substances 0.000 description 1
- 230000003116 impacting effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000001000 micrograph Methods 0.000 description 1
- 230000000116 mitigating effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000013515 script Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
- G06F18/2193—Validation; Performance evaluation; Active pattern learning techniques based on specific statistical tests
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Multimedia (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Biotechnology (AREA)
- Computing Systems (AREA)
- Library & Information Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biochemistry (AREA)
- Chemical & Material Sciences (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910491782.6A CN110363218B (zh) | 2019-06-06 | 2019-06-06 | 一种胚胎无创评估方法及装置 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910491782.6A CN110363218B (zh) | 2019-06-06 | 2019-06-06 | 一种胚胎无创评估方法及装置 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN110363218A true CN110363218A (zh) | 2019-10-22 |
CN110363218B CN110363218B (zh) | 2023-07-11 |
Family
ID=68215753
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910491782.6A Active CN110363218B (zh) | 2019-06-06 | 2019-06-06 | 一种胚胎无创评估方法及装置 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110363218B (zh) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111539308A (zh) * | 2020-04-20 | 2020-08-14 | 浙江大学 | 基于深度学习的胚胎质量综合评价装置 |
WO2022012110A1 (zh) * | 2020-07-17 | 2022-01-20 | 中山大学 | 胚胎光镜图像中细胞的识别方法及***、设备及存储介质 |
CN115641364A (zh) * | 2022-12-22 | 2023-01-24 | 武汉互创联合科技有限公司 | 基于胚胎动力学参数的胚胎***周期智能预测***及方法 |
CN115641335A (zh) * | 2022-12-22 | 2023-01-24 | 武汉互创联合科技有限公司 | 基于时差培养箱的胚胎异常多级联智能综合分析*** |
Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007166966A (ja) * | 2005-12-21 | 2007-07-05 | Osaka Bioscience Institute | 染色体異常疾患のモデル動物、その作製方法およびそれを用いた治療剤の評価方法 |
US20110092763A1 (en) * | 2008-05-27 | 2011-04-21 | Gene Security Network, Inc. | Methods for Embryo Characterization and Comparison |
US20110092762A1 (en) * | 2009-08-22 | 2011-04-21 | The Board Of Trustees Of The Leland Stanford Junior University | Imaging and evaluating embryos, oocytes, and stem cells |
CN103460038A (zh) * | 2011-02-23 | 2013-12-18 | 里兰斯坦福初级大学理事会 | 检测人类胚胎中的非整倍性的方法 |
CN104004817A (zh) * | 2013-02-22 | 2014-08-27 | 哈佛大学 | 利用极体或胚胎的单细胞基因组测序来选择试管婴儿的胚胎 |
CN106104571A (zh) * | 2014-03-14 | 2016-11-09 | 尤尼森斯繁殖技术公司 | 用于分析胚胎发育的方法和设备 |
CN106520940A (zh) * | 2016-11-04 | 2017-03-22 | 深圳华大基因研究院 | 一种染色体非整倍体和拷贝数变异检测方法及其应用 |
US20170140535A1 (en) * | 2014-07-01 | 2017-05-18 | Institut National De La Santé Et De La Recherche Médicale (Inserm) | Methods for three dimensional reconstruction and determining the quality of an embryo |
RU2657769C1 (ru) * | 2017-05-31 | 2018-06-15 | Федеральное государственное бюджетное учреждение "Национальный медицинский исследовательский центр акушерства, гинекологии и перинатологии имени академика В.И. Кулакова" Министерства здравоохранения Российской Федерации | Способ прогнозирования наличия хромосомных аномалий в эмбрионах удовлетворительного и плохого качества на основании оценки транскрипционного профиля в кумулюсных клетках в программе экстракорпорального оплодотворения |
CN108550133A (zh) * | 2018-03-02 | 2018-09-18 | 浙江工业大学 | 一种基于Faster R-CNN的癌细胞检测方法 |
US20190042958A1 (en) * | 2016-01-28 | 2019-02-07 | Gerard Letterie | Automated image analysis to assess reproductive potential of human oocytes and pronuclear embryos |
-
2019
- 2019-06-06 CN CN201910491782.6A patent/CN110363218B/zh active Active
Patent Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2007166966A (ja) * | 2005-12-21 | 2007-07-05 | Osaka Bioscience Institute | 染色体異常疾患のモデル動物、その作製方法およびそれを用いた治療剤の評価方法 |
US20110092763A1 (en) * | 2008-05-27 | 2011-04-21 | Gene Security Network, Inc. | Methods for Embryo Characterization and Comparison |
US20110092762A1 (en) * | 2009-08-22 | 2011-04-21 | The Board Of Trustees Of The Leland Stanford Junior University | Imaging and evaluating embryos, oocytes, and stem cells |
CN102576027A (zh) * | 2009-08-22 | 2012-07-11 | 里兰斯坦福初级大学理事会 | 成像并评估胚胎、***和干细胞 |
CN103460038A (zh) * | 2011-02-23 | 2013-12-18 | 里兰斯坦福初级大学理事会 | 检测人类胚胎中的非整倍性的方法 |
CN104004817A (zh) * | 2013-02-22 | 2014-08-27 | 哈佛大学 | 利用极体或胚胎的单细胞基因组测序来选择试管婴儿的胚胎 |
CN106104571A (zh) * | 2014-03-14 | 2016-11-09 | 尤尼森斯繁殖技术公司 | 用于分析胚胎发育的方法和设备 |
US20170140535A1 (en) * | 2014-07-01 | 2017-05-18 | Institut National De La Santé Et De La Recherche Médicale (Inserm) | Methods for three dimensional reconstruction and determining the quality of an embryo |
US20190042958A1 (en) * | 2016-01-28 | 2019-02-07 | Gerard Letterie | Automated image analysis to assess reproductive potential of human oocytes and pronuclear embryos |
CN106520940A (zh) * | 2016-11-04 | 2017-03-22 | 深圳华大基因研究院 | 一种染色体非整倍体和拷贝数变异检测方法及其应用 |
RU2657769C1 (ru) * | 2017-05-31 | 2018-06-15 | Федеральное государственное бюджетное учреждение "Национальный медицинский исследовательский центр акушерства, гинекологии и перинатологии имени академика В.И. Кулакова" Министерства здравоохранения Российской Федерации | Способ прогнозирования наличия хромосомных аномалий в эмбрионах удовлетворительного и плохого качества на основании оценки транскрипционного профиля в кумулюсных клетках в программе экстракорпорального оплодотворения |
CN108550133A (zh) * | 2018-03-02 | 2018-09-18 | 浙江工业大学 | 一种基于Faster R-CNN的癌细胞检测方法 |
Non-Patent Citations (4)
Title |
---|
庞保珍等: "《实用中西医生殖医学》", 30 September 2018, 中医古籍出版社, pages: 609 - 610 * |
彭瑞云等: "《形态计量与图像分析学》", 31 August 2012, 军事医学科学出版社, pages: 54 - 57 * |
李楠等: "早期胚胎质量评估:提高预测胚胎发育潜能的敏感性和特异性", 《中国组织工程研究》 * |
李楠等: "早期胚胎质量评估:提高预测胚胎发育潜能的敏感性和特异性", 《中国组织工程研究》, vol. 18, no. 42, 8 October 2014 (2014-10-08), pages 6849 - 6855 * |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111539308A (zh) * | 2020-04-20 | 2020-08-14 | 浙江大学 | 基于深度学习的胚胎质量综合评价装置 |
CN111539308B (zh) * | 2020-04-20 | 2023-05-23 | 浙江大学 | 基于深度学习的胚胎质量综合评价装置 |
WO2022012110A1 (zh) * | 2020-07-17 | 2022-01-20 | 中山大学 | 胚胎光镜图像中细胞的识别方法及***、设备及存储介质 |
CN115641364A (zh) * | 2022-12-22 | 2023-01-24 | 武汉互创联合科技有限公司 | 基于胚胎动力学参数的胚胎***周期智能预测***及方法 |
CN115641335A (zh) * | 2022-12-22 | 2023-01-24 | 武汉互创联合科技有限公司 | 基于时差培养箱的胚胎异常多级联智能综合分析*** |
Also Published As
Publication number | Publication date |
---|---|
CN110363218B (zh) | 2023-07-11 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN110516596B (zh) | 基于Octave卷积的空谱注意力高光谱图像分类方法 | |
Zanjani et al. | Stain normalization of histopathology images using generative adversarial networks | |
CN109902732B (zh) | 车辆自动分类方法及相关装置 | |
CN110363218A (zh) | 一种胚胎无创评估方法及装置 | |
Woźniak et al. | Adaptive neuro-heuristic hybrid model for fruit peel defects detection | |
CN108846426B (zh) | 基于深度双向lstm孪生网络的极化sar分类方法 | |
CN108734138B (zh) | 一种基于集成学习的黑色素瘤皮肤病图像分类方法 | |
CN109241982A (zh) | 基于深浅层卷积神经网络的目标检测方法 | |
CN108764308A (zh) | 一种基于卷积循环网络的行人重识别方法 | |
CN114998220B (zh) | 一种基于改进的Tiny-YOLO v4自然环境下舌像检测定位方法 | |
CN106023257A (zh) | 一种基于旋翼无人机平台的目标跟踪方法 | |
CN112529005B (zh) | 基于语义特征一致性监督金字塔网络的目标检测方法 | |
CN109344845A (zh) | 一种基于Triplet深度神经网络结构的特征匹配方法 | |
CN108764250A (zh) | 一种运用卷积神经网络提取本质图像的方法 | |
CN112633257A (zh) | 基于改良卷积神经网络的马铃薯病害识别方法 | |
CN109919246A (zh) | 基于自适应特征聚类和多重损失融合的行人重识别方法 | |
CN110827312A (zh) | 一种基于协同视觉注意力神经网络的学习方法 | |
Chen et al. | Agricultural remote sensing image cultivated land extraction technology based on deep learning | |
CN111340019A (zh) | 基于Faster R-CNN的粮仓害虫检测方法 | |
CN112926652A (zh) | 一种基于深度学习的鱼类细粒度图像识别方法 | |
CN103279944A (zh) | 一种基于生物地理优化的图像分割方法 | |
CN113435254A (zh) | 一种基于哨兵二号影像的耕地深度学习提取方法 | |
CN115131503A (zh) | 一种虹膜三维识别的健康监测方法及其*** | |
Kiratiratanapruk et al. | Automatic detection of rice disease in images of various leaf sizes | |
CN113298007B (zh) | 一种小样本sar图像目标识别方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
TR01 | Transfer of patent right | ||
TR01 | Transfer of patent right |
Effective date of registration: 20240528 Address after: 400013 No. 64, Jintang street, Yuzhong District, Chongqing Patentee after: Chongqing maternal and Child Health Hospital (Chongqing obstetrics and Gynecology Hospital, Chongqing Institute of genetics and reproduction) Country or region after: China Address before: 430000 No.1 Workshop, 1st floor, building B10, Wuhan hi tech medical equipment Park, 818 Gaoxin Avenue, Donghu New Technology Development Zone, Wuhan City, Hubei Province Patentee before: Zhang Xiaodong Country or region before: China |