A method for imaging expected results of a medical cosmetic treatment includes converting an input image of an anatomical feature into an input image vector. A direction vector corresponding to the medical cosmetic treatment is determined. An amplitude of the direction vector is determined. The direction vector is multiplied by the determined amplitude to obtain a product vector. The product vector is vector added to the input image vector. An output image corresponding to the expected visual appearance of the human anatomical feature is generated from the vector added product vector and input image vector. A computer program stored in a non-transitory computer readable medium causes a computer to perform the imaging method.

The technology is provided to effectively visualize culture statuses related to a plurality of culture targets. Provided is a control device including a display control unit that controls dynamic display related to a culture status of a culture target including a cell having a division potential, the culture status being estimated along a time series by morphological analysis using a learned model generated on the basis of a machine learning algorithm, in which the display control unit controls comparative display of the culture statuses of a plurality of the culture targets. Furthermore, provided is a control method including controlling, by a processor, dynamic display related to a culture status of a culture target including a cell having a division potential, the culture status being estimated along a time series by morphological analysis using a learned model generated on the basis of a machine learning algorithm, and controlling the display further including controlling comparative display of the culture statuses of a plurality of the culture targets.

The disclosure provides for a method for generating an ultrasound image that includes transmitting, by a plurality of transmitters in a transducer, at least two transmit beams at different angles, where at least parts of the transmit beams cover an overlapping region, and receiving, by a plurality of sensors of the transducer, reflected signals of the transmit beams. The method further comprises calculating channel coherence for the received signals to produce one or more channel coherence images, and calculating transmit coherence for the received signals to produce one or more transmit coherence images. The information from at least one of the channel coherence images and at least one of the transmit coherence images are combined to identify moving objects. The received signals from different transmits in overlapping regions are then processed to produce a final image that is compensated for the moving objects.

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

Disclosed is a system and a method for monitoring a CT scan image. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A region of interest may be identified on each slice using an image processing technique. The region of interest may be masked on each slice using deep learning. Subsequently, a nodule may be detected as the region of interest using the deep learning. Further, a plurality of characteristics associated with the nodule may be identified. Furthermore, an emphysema may be detected in the region of interest on each slice. A malignancy risk score for the patient may be computed. A progress of the nodule may be monitored across subsequent CT scan images. Finally, a report of the patient may be generated.

Provided is a system, method, and computer program product for analyzing spatio-temporal medical images using an artificial neural network. The method includes capturing a series of medical images of a patient, the series of medical images comprising visual movement of at least one entity, tracking time-varying spatial data associated with the at least one entity based on the visual movement, generating spatio-temporal data by correlating the time-varying spatial data with the series of medical images, and analyzing the series of medical images based on an artificial neural network comprising a plurality of layers, one or more layers of the plurality of layers each combining features from at least three different scales, at least one layer of the plurality of layers of the artificial neural network configured to learn spatio-temporal relationships based on the spatio-temporal data.

At least one example embodiment relates to a computer-implemented method comprising receiving a medical image dataset; receiving at least one medical comparison image dataset; extracting biometric data based on the medical image dataset; extracting biometric comparison data based on the medical comparison image dataset; determining a measure of difference between the biometric comparison data and the biometric data; and assigning the medical image dataset to the medical comparison image dataset when the measure of difference does not exceed a threshold value.

An object of the present invention is to provide a medical image processing apparatus, an endoscope system, a medical image processing method, and a program that assist a user in estimating an accurate size of a region of interest. A medical image processing apparatus according to an aspect of the present invention is a medical image processing apparatus including a processor. The processor is configured to acquire images in time-series, make a determination of whether a region of interest in each of the images is suitable for size estimation, and report, by using a reporting device, a result of the determination and operation assistance information for improving the result of the determination.

A medical information display apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to display information on the basis of volume data including at least a lung. The processing circuitry is configured to display a first index value based on an anatomical structure of the lung and a second index value based on the volume data related to the lung, so as to be kept in correspondence with each other.

A method for evaluating the effectiveness of a skin treatment on a skin feature includes correcting the color of a first image before the skin treatment, correcting the color of a second image after the skin treatment, determining the sizes of the skin feature in the first and second images, and comparing the corrected colors and sizes of the skin feature in the first and second images. In some embodiments, the first and second images include the skin feature and an indicator adjacent to the skin feature, where the indicator has a standard color and a known size.