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.

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

An information processing apparatus includes an acquisition unit, a determination unit, and an output unit. The acquisition unit acquires history information of image processing using a plurality of medical images. The determination unit determines, using the acquired history information, whether each of a plurality of candidate images that are candidates of a second medical image used for image processing together with a first medical image selected by an operator has already been processed. The output unit outputs a notification in accordance with the result of the determination.

A method of modeling lungs of a patient includes acquiring computed tomography data of a patient's lungs, storing a software application within a memory associated with a computer, the computer having a processor configured to execute the software application, executing the software application to differentiate tissue located within the patient's lung using the acquired CT data, generate a 3-D model of the patient's lungs based on the acquired CT data and the differentiated tissue, apply a material property to each tissue of the differentiated tissue within the generated 3-D model, generate a mesh of the 3-D model of the patient's lungs, calculate a displacement of the patient's lungs in a collapsed state based on the material property applied to the differentiated tissue and the generated mesh of the generated 3-D model, and display a collapsed lung model of the patient's lungs based on the calculated displacement of the patient's lungs.

An X-ray imaging system using multiple puked X-ray sources to perform highly efficient and ultrafast 3D radiography is presented. There are multiple puked X-ray sources mounted on a structure in motion to form an array of sources. The multiple X-ray sources move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Electron beam inside each individual X-ray tube is deflected by magnetic or electrical field to move focal spot a small distance. When focal spot of an X-ray tube beam has a speed that is equal to group speed but with opposite moving direction, the X-ray source and X-ray flat panel detector are activated through an external exposure control unit so that source tube stay momentarily standstill equivalently. 3D scan can cover much wider sweep angle in much shorter time and image analysis can also be done in real-time.

The present invention relates to training data sets and a system and method for generating training images especially those which are medical images. Especially disclosed is a method of training a machine learning model to recognize movement of a body part in an acquired medical image The machine learning model is trained by varying/modifying a blur convolution kernel constructed with pixels oriented in a direction of the movement; the method including determining at least one motion weighting factor corresponding to a motion time period when the body part is moving during acquisition of the medical image, and using the motion weighting factor to vary/modify the blur convolution kernel.

Embodiments of the disclosure provide methods, apparatuses, and computer-readable media for generating image reports. In one embodiment, the method includes: obtaining an image to be analyzed; determining at least one reference image corresponding to the image to be analyzed, the at least one reference image corresponding to a respective reference image report; and generating, based on the reference image report, an image report corresponding to the image to be analyzed. In the method for generating an image report provided by the embodiments, by obtaining an image to be analyzed is obtained, at least one reference image corresponding to the image to be analyzed is determined; and an image report corresponding to the image to be analyzed is generated based on a respective reference image report corresponding to the at least one reference image. Therefore, medical staff are assisted in quickly writing an image report, thereby ensuring the quality and the efficiency of the image report, reducing labor costs and time costs required for collating the image report, and further improving the practicability of the method.

Some methods of analyzing one or more brain lesions of a patient comprise, for each of the lesion(s), calculating one or more lesion characteristics from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time. The characteristic(s) can include a change, form the first time to the second time, in the lesion's volume and/or surface area, the lesion's displacement from the first time to the second time, and/or the lesion's theoretical radius ratio at each of the first and second times. Some methods comprise characterizing whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s).

Various embodiments are directed to video-based deep learning evaluation of cardiac ultrasound that accurately identify cardiomyopathy and predict ejection fraction, the most common metric of cardiac function. Embodiments include systems and methods for analyzing images obtained from an echocardiogram. Certain embodiments include receiving video from a cardiac ultrasound of a patient illustrating at least one view the patient's heart, segmenting a left ventricle in the video, and estimating ejection fraction of the heart. Certain embodiments include at least one machine learning algorithm.