公开(公告)号：EP3523649B1

公开(公告)日：2020-11-11

申请号：EP18796987.8

申请日：2018-11-13

申请人： Technische Universität München

发明人： GÓMEZ, Pedro A. ,  MOLINA-ROMERO, Miguel ,  HAASE, Axel ,  SCHUSSER, Benjamin ,  AIGNER, Maximilian ,  LAPARIDOU, Maria

IPC分类号： G01N33/08 ,  G01N24/08 ,  G01R33/483

公开(公告)号：EP3798659A1

公开(公告)日：2021-03-31

申请号：EP20206710.4

申请日：2018-11-13

申请人： Technische Universität München

发明人： GÓMEZ, Pedro A. ,  MOLINA-ROMERO, Miguel ,  HAASE, Axel ,  SCHUSSER, Benjamin ,  AIGNER, Maximilian ,  LAPARIDOU, Maria

IPC分类号： G01R33/483 ,  G01N33/08 ,  G01N33/483

摘要： Shown herein is a method of automated noninvasive determining the fertility of a bird's egg (14), comprising the following steps:
conveying a plurality of bird eggs (14) sequentially or in parallel into an NMR apparatus (18),
subjecting the bird eggs (14) to an NMR measurement, such as to generate a 3-D NMR image of at least a part of each of said eggs (14), said 3-D NMR image having a spatial resolution in at least one dimension of 1.0 mm or less, preferably of 0.50 mm or less, wherein said part of the egg (14) includes the germinal disc of the respective egg (14), determining a prediction of the fertility according to at least one of the following two procedures:
(i) deriving at least one feature from each of said 3-D NMR images, and employing said at least one feature in a feature-based classifier for determining a prediction of the fertility, and
(ii) using a deep learning algorithm, and in particular a deep learning algorithm based on convolutional neural networks, generative adversarial networks, recurrent neural networks or long short-term memory networks.

公开(公告)号：EP3523649A1

公开(公告)日：2019-08-14

申请号：EP18796987.8

申请日：2018-11-13

申请人： Technische Universität München

发明人： GOMEZ, Pedro A. ,  MOLINA-ROMERO, Miguel ,  HAASE, Axel ,  SCHUSSER, Benjamin ,  AIGNER, Maximilian ,  LAPARIDOU, Maria

IPC分类号： G01N33/08 ,  G01N24/08 ,  G01R33/483

摘要： Shown herein is a method of automated noninvasive determining the fertility of a bird's egg (14), comprising the following steps: conveying a plurality of bird eggs (14) sequentially or in parallel into an NMR apparatus (18), subjecting the bird eggs (14) to an NMR measurement, such as to generate a 3-D NMR image of at least a part of each of said eggs (14), said 3-D NMR image having a spatial resolution in at least one dimension of 1.0 mm or less, preferably of 0.50 mm or less, wherein said part of the egg (14) includes the germinal disc of the respective egg (14), determining a prediction of the fertility according to at least one of the following two procedures: (i) deriving at least one feature from each of said 3-D NMR images, and employing said at least one feature in a feature-based classifier for determining a prediction of the fertility, and (ii) using a deep learning algorithm, and in particular a deep learning algorithm based on convolutional neural networks, generative adversarial networks, recurrent neural networks or long short-term memory networks.