Methods and systems for implementing health monitoring based on changes in body dimensions derived from images of a human collected over long periods of time are presented herein. An image based medical diagnosis system precisely measures body dimensions, changes in body dimensions over time, or both, and identifies whether any measured dimensions, changes in dimensions, or both, have exceeded one or more threshold values indicative of the onset or recurrence of a medically significant condition. In another further aspect, an alert is communicated to medical professionals to further investigate the medical condition of the human user. In one aspect, body dimensions are estimated based on a known value of a reference dimension identifiable in each of the plurality of images. In some examples, the distance between facial features in an image is normalized with respect to the measured inter-pupillary distance associated with that image.

A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

A chest x-ray differential diagnosis system is operable to generate abnormality pattern data is generated for each of a received plurality of chest x-rays by identifying at least one pattern in each chest x-ray corresponding to an abnormality by utilizing a computer vision model that is trained on a plurality of training chest x-rays. Differential diagnosis data is generated for each chest x-ray based on the abnormality pattern data. Filtering parameters are received from a client device, and a filtered chest x-ray queue that includes a subset of chest x-rays is selected based on the filtering parameters and the differential diagnosis data is generated for transmission to the client device for display. Differential diagnosis data corresponding a chest x-ray indicated in chest x-ray selection data received from the client device is transmitted to the client device for display via the display device in conjunction with the chest x-ray.

There is provided an embryonic development analysis system, including: processing circuitry configured to: recognize a shape of one or more cells represented in one or more of a plurality of embryonic development images captured in a time series; calculate a time-series change of the shape of the one or more cells based on the recognized shape of the one or more cells in the one or more of the plurality of embryonic development images; and calculate a first feature amount based on the calculated time-series change of the shape.

The invention relates to a method for evaluating treatment-relevant spatial anatomical information among different data sets of the heart, the method comprising the steps of: determining a reference anatomical 3 dimensional data set of the heart, providing a first anatomical 3 dimensional data set of the heart, the first anatomical 3 dimensional data set comprising first treatment-relevant spatial anatomical information, providing a second anatomical 3 dimensional data set of the heart, the second anatomical 3 dimensional data set comprising second treatment-relevant spatial anatomical information, registering the reference data set to the first and the second data sets, transferring the treatment relevant spatial anatomical information of the first and the second data set to the reference data set in order to generate a first transferred treatment-relevant spatial anatomical information on the reference data set and a second transferred treatment-relevant spatial anatomical information on the reference data set evaluating the first and the second transferred treatment-relevant spatial anatomical information.

A stroke detection system analyzes images of a person's face over time to detect asymmetric changes in the position of certain reference points that are consistent with sagging or drooping that may be symptomatic of a stroke or TIA. On detecting possible symptoms of a stroke or TIA, the system may alert caregivers or others, and log the event in a database. Identifying stroke symptoms automatically may enable more rapid intervention, and identifying TIA symptoms may enable diagnostic and preventative care to reduce the risk of a future stroke.

A medical-information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires medical image data that is obtained during imaging on the subject in a resting state in the time phase where the relationship between the volume of blood flow and the pressure in a blood vessel in the cardiac cycle of the subject indicates a proportional relationship. The processing circuitry extracts the structure of a blood vessel, included in the medical image data, applies fluid analysis to the structure of the blood vessel to obtain a first index value, which is obtained based on the pressure in the blood vessel on the upstream side of a predetermined position within the blood vessel and the relation equation between the volume of blood flow and the pressure in the blood vessel in the resting state, and a second index value, which is obtained based on the pressure in the blood vessel on the downstream side of the predetermined position and the relation equation, and calculates the pressure ratio, which is the ratio of the first index value to the second index value.

A method is described for determination of an initialization time point of imaging of a region of interest of an object to be examined. A slice of the object is selected for bolus-tracking images in which the flow of a fluid flowing toward the region of interest is observable. A plurality of bolus-tracking images of the slice is taken. Time-density curves are determined based upon intensity values assigned to the individual image points acquired in the plurality of bolus-tracking images for the selected slice. Individual image points are divided into groups according to similarity of time-density curves assigned to the individual image points. Finally, the time at which an intensity value assigned to one of the groups exceeds a threshold value is determined. Also described are a method for carrying out the imaging of a region of interest; an initialization time point determination device; and a computed tomography system.

The disclosure relates to a method for automatically recognizing liver tumor types in ultrasound images. The method specifically comprises: using a plurality of Regions of Interest (ROIs) to represent a CEUS image; different lesions are distinguished by the performance and changes of the ROI in time and space; representing a space-time relationship between ROIs by establishing a model in time and space at the same time; and determining, by the model, a relatively appropriate ROI and relevant parameters of the model according to existing CEUS lesion samples by means of an iterative learning method. After giving a sample, an appropriate ROI can be determined and a reference diagnosis for the lesion can be given by removing part of inappropriate ROIs in advance and by means of a rapid search method.

A method of tracking a cell through a plurality of images includes selecting the cell in at least one image obtained at a first time, generating a track of the cell through a plurality of images, including the at least one image, obtained at different times using a backward tracking, and generating a cell tree lineage of the cell using the track.