Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

The invention relates to a method, an image processor (26) and a medical observation device (1), such as a microscope or endoscope, for observing an object (4) containing a bolus of at least one fluorophore (12). The object (4) is preferably live tissue comprising several types (16, 18, 20) of tissue. According to the method, a set (34) of component signals (36) is provided. Each component signal (36) represents a fluorescence intensity development of the fluorophore (12) over time in a different type of tissue. A time series (8) of input frames (10) is accessed, one input frame (10) after the other. The input frames (10) represent electronically coded still images of the object (4) at subsequent time. Each input frame (10) contains at least one observation area (22) comprising at least one pixel (23). In the observation area (22) of the current input frame (10) of the time series (8), a fluorescent light intensity (I) is determined over at least one fluorescence emission wavelength (15) of the fluorophore (12). This fluorescent light intensity (I.sub.1) is joined with the fluorescence light intensities (I.sub.n) of the observation area (22) of preceding input frames (10) of the time series (8) to generate a time sequence (40) of fluorescent light intensities (I.sub.1, I.sub.n) of the observation area (22). This time sequence (40) is decomposed on in a preferably linear combination (72) of at least some of the component signals (36) of the set (34). A new set (34) of component signals (36) is provided which includes only those component signals (36) which are present in the combination (72). An output frame (46) is generated, in which the observation area (22) is assigned a color from a color space depending on the combination (72) of component signals (36).

Disclosed herein are platforms, systems, and methods including a cell culture system that includes a cell culture container comprising a cell culture, the cell culture receiving input cells, a cell imaging subsystem configured to acquire images of the cell culture, a computing subsystem configured to perform a cell culture process on the cell culture according to the images acquired by the cell imaging subsystem, and a cell editing subsystem configured to edit the cell culture to produce output cell products according to the cell culture process.

The present invention is to provide a computer system, a method, and a program for diagnosing a subject that improve the accuracy of diagnosis by combining a plurality of time-series image data more than that by a conventional single image analysis. The computer system for diagnosing a subject acquires a plurality of first subject images with time series variation of the subject, analyzes the acquired first subject images, acquires a plurality of second subject images with time series variation of another subject in the past, analyzes the acquired second subject images, checks the analysis result of the first subject images and the analysis result of the second subject images, and diagnoses the subject based on the check result.

A system and method of classifying images of pathology. An image is received, normalized, and segmented normalizing the image; into a plurality of regions; A disease vector is automatically determining for the plurality of regions with at least one classifier comprising a neural network. Each of the respective plurality of regions is automatically annotated, based on the determined disease vectors. The received image is automatically graded based on at least the annotations. The neural network is trained based on at least an expert annotation of respective regions of images, according to at least one objective classification criterion. The images may be eye images, vascular images, or funduscopic images. The disease may be a vascular disease, vasculopathy, or diabetic retinopathy, for example.

There is provided a computer system for evaluating the quality of a fertile ovum. The computer system includes computer processing circuitry configured to receive a plurality of images of a fertile ovum captured in time-series by an imaging apparatus, provide as input to at least one learned model, the plurality of images of the fertile ovum or information based on the plurality of images of the fertile ovum, wherein the at least one learned model has been trained to output, based at least in part, on the plurality of images, fertile ovum analysis information describing characteristics of the fertile ovum used to evaluate a quality of fertile ovum, and provide evaluation support information based, at least in part, on the fertile ovum analysis information, wherein the evaluation support information enables a quality evaluator to interact with the web dashboard to modify at least some of the evaluation support information.

The present disclosure describes ultrasound imaging systems and methods configured to delineate sub-regions of bodily tissue within a target region and determine a biopsy path for sampling the tissue. Systems may include an ultrasound transducer configured to image a biopsy plane within a target region. A processor communicating with the transducer can obtain a time series of sequential data frames associated with echo signals acquired by the transducer and apply a neural network to the data frames. The neural network can determine spatial locations and identities of various tissue types in the data frames. A spatial distribution map labeling the coordinates of the tissue types identified within the target region can also be generated and displayed on a user interface. The processor can also receive user input, the neural network determines spatial locations and identities of a plurality of via the user interface, indicating a targeted biopsy sample to be collected, which can be used to determine a corrected biopsy path.

A vehicle control device includes a tracking unit estimating a motion of a moving object, a model selection unit selecting a motion model corresponding to a moving object type, an abnormality determination unit determining a presence or absence of an abnormality of the estimation of the motion of the moving object based on the estimated moving object motion and the motion indicated by the motion model, and a control unit. A control mode in which the control unit controls traveling of a host vehicle when the abnormality determination unit determines that the abnormality is present differs from a control mode in which the control unit controls the traveling of the host vehicle when the abnormality determination unit determines that the abnormality is absent.

A foot screening system configured to aid in screening a plantar surface of a foot of a user for sores, ulcers, and other signs of diseases. The foot screening system including a foot platform including a foot contacting surface configured to serve as a foot stabilizing device during a screening of a plantar surface of a foot of a user, a camera stabilizer platform configured to support a mobile computing device at a desired angle and distance from the foot platform, and a user interface configured to aid a user in capturing one or more images of the plantar surface of the foot of the user, flagging the one or more images for additional review, and uploading the one or more images to a network for access by a healthcare provider.

Systems and methods are described herein for monitoring and capturing information associated with target subjects, such as babies. For example, the systems and methods provide a capture device, such as a camera, that captures capture time of flight (TOF) data from a target subject. Using the TOF data, the systems and methods, via a display device associated with (e.g., paired to) the capture device, present information about the target subject, such as information indicating movement of the target subject, information indicating a breathing rate of the target subject, information indicating a heart rate of the target subject, and so on.