A system for determining the viability of an embryo comprises an imaging device, an excitation device configured to direct an excitation energy at an embryo, a controller communicatively connected to the imaging device and the excitation device, configured to drive the excitation device and collect images from the imaging device at an imaging frequency, a processor performing steps comprising acquiring a set of images from the imaging device, performing a Fourier Transformation to generate a set of phasor coordinates, computing a D-trajectory, computing a set of values of additional parameters, comparing the set of values to a set of stored values related to embryos of known viability, and calculating a viability index factor of the embryo from the set of values and the set of stored values. Methods of calculating embryo viability and determining one or more properties of a tissue are also described.

A multi-media product is created from fetal ultrasound images, during scanning of a fetus using an ultrasound scanner, and employs a specifically trained artificial intelligence (AI) model to execute on a computing device communicably connected to an ultrasound scanner, wherein the AI model is trained so that when the AI model is deployed, the computing device identifies and selects one or more fetal anatomical features, in whole or part, imaged in fetal ultrasound imaging data generated during ultrasound scanning as part of a clinical exam of the fetus, wherein the selected one or more fetal anatomical features are visually appealing for entertainment and keepsake purposes and are not part of a clinical assessment of the health or growth of the fetus and wherein after acquiring a new fetal ultrasound image during ultrasound scanning, the AI model selects the one or more fetal anatomical features, in whole or part, which are visually appealing for entertainment and keepsake purposes and are not part of a clinical assessment of the health or growth of the fetus and those selected non-clinical images are then used to generate the entertainment focused multi-media product.

A computer-implemented method for visualization of an elongated anatomical structure (20), for example of a fetal spine using ultrasound is provided. The method comprising the steps of: receiving a plurality of 3D ultrasound image volumes, each image volume depicting at least a portion of an elongated anatomical structure (20); on each 3D ultrasound image volume, automatically or semi-automatically fitting a parametric curve (30) to the depicted portion of the elongated anatomical structure, the parametric curve being defined by curve parameters; reformatting each 3D ultrasound image volume by applying a transformation which straightens the parametric curve along at least one axis, so as to generate a plurality of reformatted image volumes and reformatted parametric curves (32, 34); registering the reformatted image volumes with one another by determining the joining point of their respective parametric curves; and fusing the reformatted image volumes with one another to yield a fused image depicting the whole elongated anatomical structure or a larger portion thereof than the 3D ultrasound image volumes.

An image processing method according to the invention comprises: obtaining three-dimensional image data representing a three-dimensional image of a fertilized egg in a cleavage stage obtained by imaging the fertilized egg by optical coherence tomography; extracting a blastomere region corresponding to an individual blastomere in the three-dimensional image for each blastomere based on the three-dimensional image data; counting a number of the blastomere region extracted in the three-dimensional image; obtaining a volume of each blastomere region based on the three-dimensional image data; and determining a number X of the extracted blastomere regions is expressed by a following inequality:

2.sup.N<X<2.sup.N+1 (N is a natural number)

and when the inequality holds, dividing each of (2.sup.N+1−X) blastomere regions having a largest volume out of the X blastomere regions equally into two blastomere regions.

An image processing method according to the present invention includes: obtaining three-dimensional image data representing a three-dimensional image of a fertilized egg in a cleavage stage obtained by imaging the fertilized egg by optical coherence tomography; extracting a blastomere region corresponding to an individual blastomere in the three-dimensional image for each blastomere based on the three-dimensional image data; obtaining a volume of each blastomere region based on a result of the extraction and the three-dimensional image data; and obtaining an index value expressing a volume variation of the blastomeres based on a derived result of the volumes. It is possible to provide useful quantitative information in evaluating a state of the fertilized egg.

Microscopy methods for determining embryo viability are described. A method can include accessing, at a compute device, fluorescence lifetime imaging microscopy (FLIM) data set associated with a biological material. The biological material can include either an embryo or a gamete. The method further includes extracting a fluorescence photon arrival time from a subset of data from the FLIM data set. The method further includes estimating a likelihood that the biological material will produce a successful pregnancy and/or a live birth based on the fluorescence photon arrival time histogram and an estimation model that has been trained using artificial intelligence and labeled clinical training data. The method includes generating an output signal representing the estimated likelihood that the biological material will produce a successful pregnancy and/or a live birth. In some embodiments, the method can include training the estimation model using a plurality of fluorescence photon arrival time histograms of the FLIM data set.

There is provided an information processing apparatus, including: an image obtaining unit configured to obtain a plurality of images of a fertile ovum captured in time series; a recognizing unit including a probability image generating unit configured to generate, for each image of the fertile ovum, a probability image, wherein each position in the probability image represents the probability that at least part of the fertile ovum is present at the corresponding position in the image of the fertile ovum; and a feature amount calculating unit configured to calculate time-series transformation of the fertile ovum from the probability images over the time series, and calculate a feature amount of the fertile ovum based on the transformation.

Systems and methods are provided for assigning a quality parameter to a reproductive cellular structure. An image of the reproductive cellular structure is obtained. The image of the reproductive cellular structure is provided to a neural network to generate a value representing a morphology of the reproductive cellular structure. The value is compared to a predefined standard to provide a quality assurance metric representing one of a medical personnel, a facility, a growth medium, and an identity of the reproductive cellular structure.

System, method and computer readable medium that assess characteristics of an embryo by processing video image data of the embryo. Video is obtained, typically from third parties, of a target embryo. The video has frame speed of two frames per second, or faster and includes image data representing morphokinetic movement of the embryo. The image data is processed using a trained machine learning model that assesses embryo characteristics. The video has a duration of ten minutes or less. The assessment can be a prediction of a likelihood the embryo is viable and/or will produce a pregnancy upon transfer into a recipient. Further, the assessment can predict a likelihood the embryo will: (1) produce offspring having a specific sex; (2) embody a genetic anomaly; (3) perpetuate desired traits in produced offspring and/or (4) produce undesired characteristics in produced offspring.

A computer-implemented system and method for predicting male sex bovine offspring to result from a bovine embryo by processing video image data of the embryo. The method includes receiving image data derived from video of a target embryo taken at substantially real-time frame speed during an embryo observation period of time. The video contains recorded morphokinetic movement of the target embryo occurring during the embryo observation period of time. The movement is represented in the received image data and the received image data is processed using a model generated utilizing machine learning and correlated embryo outcome data.