An ultrasound diagnostic apparatus 1 includes an image acquisition unit 3 that generates an ultrasound image, an image recognition unit 9 that performs image recognition for the ultrasound image to calculate recognition scores, an index value calculation unit 10 that calculates index values of a plurality of parts on the basis of the recognition scores calculated for a predetermined number of ultrasound images, a part narrowing-down unit 11 that narrows down target parts for which part determination is to be performed, from the plurality of parts on the basis of the index values, and a part determination unit 12 that determines an imaging part of the subject on the basis of the recognition scores calculated by the image recognition unit 9 for the target parts narrowed down by the part narrowing-down unit 11.

There is provided a method of processing 2D ultrasound images for computing clinical parameter(s) of a right ventricle (RV), comprising: selecting one 2D ultrasound image of 2D ultrasound images depicting the RV, interpolating an inner contour of an endocardial border of the RV for the selected 2D image, tracking the interpolated inner contour obtained for the one 2D ultrasound image over the 2D images over cardiac cycle(s), computing a RV area of the RV for each respective 2D image according to the tracked interpolated inner contour, identifying a first 2D image depicting an end-diastole (ED) state according to a maximal value of the RV area for the 2D images, and a second 2D US image depicting an end-systole (ES) state according to minimal value of the RV area for the 2D images, and computing clinical parameter(s) of the RV according to the identified first and second 2D images.

A method for studying the behavior of an animal in an experimental area including stimulating the animal using a stimulus device; collecting data from the animal using a data collection device; analyzing the collected data; and developing a quantitative behavioral primitive from the analyzed data. A system for studying the behavior of an animal in an experimental area including a stimulus device for stimulating the animal; a data collection device for collecting data from the animal; a device for analyzing the collected data; and a device for developing a quantitative behavioral primitive from the analyzed data. A computer implemented method, a computer system and a nontransitory computer readable storage medium related to the same. Also, a method and apparatus for automatically discovering, characterizing and classifying the behavior of an animal in an experimental area. Further, use of a depth camera and/or a touch sensitive device related to the same.

Methods and systems for aiding in placement of surgical intervention sites for treating lesions for optimization of surgical outcomes are provided. Also disclosed are computer-aided software packages for identifying the optimal placement of a surgical intervention on a patient in need thereof. The packages, systems, and methods for optimizing placement provided herein help to achieve superior results (e.g. increased drainage), decrease hospitalization time, reduce recurrence of lesions and need for drainage, and improve cognitive outcomes for patients.

Methods of generating a metric to quantitatively represent an effect of a treatment are disclosed. In one arrangement, first and second sample data units are received, each representing a segmented image of a biological sample taken from a subject. The segmentation divides the image into plural segmentation sets of regions. Each of the first and second sample data units is analysed to determine information about a spatial distribution of biomarkers relative to the segmentation sets. A metric is generated using a combination of the determined information about the spatial distribution of biomarkers relative to the segmentation sets for the first and second sample data units.

The invention relates to a method for evaluating in post-treatment an ablation of a portion of an anatomy of interest of an individual, the anatomy of interest comprising at least one lesion. The post-treatment evaluation method comprises in particular a step of automatically evaluating a risk of recurrence of the lesion of the anatomy of interest of the individual based on the analysis of a pair of pre-operative and post-operative medical images of the anatomy of interest of the individual by means of automatic learning method of the neural network type, said method being preloaded during a so-called training phase on a database comprising a plurality of pairs of medical images of an anatomy of interest identical to a plurality of individuals, each medical image pair of the database being associated with a recurrence status of a lesion of the anatomy of interest of said patient. The invention also relates to an electronic device comprising a processor and a computer memory storing instructions of such an evaluation method.

The present invention relates to a plant growth identification method and a system therefor. At least one photographing unit transmits a picture captured thereby to a control unit so as to calculate the pixel distance per unit, and then the photographing unit captures a picture of a plant so that features points of the plant such as buds, flowers, or fruits in the captured picture are identified with the control unit, the pixel distance of each feature point and the height thereof are then calculated with the control unit, and the number and area of the multiple feature points are counted so as to calculate the growth rate and yield of the plant. Moreover, data such as growth height, rate and yield of the plant is regularly updated so as to dynamically adjust the light intensity of a lighting unit on the plant.

The present disclosure relates to a system (100) for scoring motion of a heart wall (214, 218). The system (100) includes an imaging system (102) operable to acquire a first image (230) of the heart wall (214, 218) at a first time and a second image (240) of the heart wall (214, 218) at a second time. A processor (108) is provided to identify a first set of contour data in the first image (230); and a second set of contour data in the second image (240). The processor (108) defines at least one element (E.sub.i) representing a cardiac cyclic change in a section of the heart wall (214, 218) in dependence on the first and second sets of contour data. Each element (E.sub.i) is analysed to generate at least one metric which is compared with a reference data model to score the motion of the corresponding section of the heart wall (214, 218). The disclosure also relates to a method of scoring motion of a heart wall (214, 218); and a non-transitory computer-readable medium.

A method includes uniquely training, using a processing device, one or more deep learning models using one or more sets of training data associated with a plurality of patients to predict immunotherapy treatment responses indicative of a patient survival rate based on a change in volume of a lesion of a patient. The method includes providing a single pre-treatment image of a target lesion of a target patient to the one or more deep learning models that are uniquely trained using the sets of training data to generate the immunotherapy treatment responses. The method includes combining the immunotherapy treatment responses of the one or more deep learning models that are uniquely trained using the sets of training data to generate a predicted treatment response score.

For generating a breathing alert is disclosed, a method receives a video stream of a subject. The method further estimates a breathing signal from the video stream. The method determines one of a large-scale motion and/or a breathing event of the subject based on the breathing signal. The method generates an alert if both no breathing event is identified and no large-scale motion of the subject is identified within an event time interval.