Aneurysms are classified and quantitatively analyzed based on medical image data acquired from a subject. In general, one or more algorithms are implemented to automatically classify, or otherwise diagnose, and measure aneurysms and their change over time. These algorithms make use of artificial intelligence and deep learning to develop quantitative analytics that can be consolidated into diagnostic reports.

The present invention provides a physiological information detection method for calculating a physiological value by using changes of a dynamic image. The detection method includes: acquiring detection data from a gray-scale value of the dynamic image, and transforming the detection data into frequency data. The detection method further includes: determining whether the frequency data meet a preset condition, and using a transformation model of a corresponding transformation combination accordingly to transform the frequency data into a physiological value. The present invention further provides a physiological information detection device applying the detection method.

Systems and methods for identifying growth of a cerebral aneurysm. The method includes forming a first virtual skeleton model from a first image segmentation and forming a second virtual skeleton model from a second image segmentation. The virtual skeleton models including a plurality of edges with each edge having a plurality of skeleton points. Each skeleton point is associated with a subset of a plurality of blood vessel surface points. The method includes identifying one or more second terminal points within the second virtual skeleton model and overlapping the first virtual skeleton model and the second virtual skeleton model by orienting the one or more first terminal points with the one or more second terminal points.

A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires a first index value obtained based on fluid analysis that is performed based on an image including a blood vessel of a subject, the first index value being related to blood flow at each of positions in the blood vessel. The processing circuitry acquires external information including a second index value related to blood flow at each of the positions in the blood vessel. The processing circuitry changes one of an arrangement direction of index values in a first graph and an arrangement direction of index values in a second graph in accordance with the other one of the arrangement directions. The processing circuitry displays the first graph and the second graph on a display unit such that the arrangement directions of the index values match each other.

Thoracic aortic aneurysm is a common and lethal disease that requires regular imaging surveillance to determine timing of surgical repair and prevent major complications such as rupture. Current cross-sectional imaging surveillance techniques, largely based on computed tomography angiography (CTA) or magnetic resonance angiography (MRA), are focused on measurement of maximal aortic diameter, although this approach is limited to fixed anatomic positions and is prone to significant measurement error. The present techniques demonstrate novel approaches (generally termed herein Vascular Deformation Mapping (VDM)) for assessing changes in aortic dimensions. The present techniques quantify three-dimensional changes in the anatomic dimensions of a vessel through a process that involves non-rigid co-registration of serial imaging data and quantification of vascular deformation on a 3D surface model using some derivation of the spatial deformations resulting from the optimized spatial transform.

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.

System and methods for monitoring the growth of an aquatic plant culture and detecting real-time characteristics associated with the aquatic plant culture aquatic plants. The systems and methods may include a control unit configured to perform an analysis of at least one image of an aquatic plant culture. The analysis may include processing at least one collected image to determine at least one physical characteristic or state of an aquatic plant culture. Systems and methods for distributing aquatic plant cultures are also provided. The distribution systems and methods may track and control the distribution of an aquatic plant culture based on information received from various sources. Systems and methods for growing and harvesting aquatic plants in a controlled and compact environment are also provided. The systems may include a bioreactor having a plurality of vertically stacked modules designed to contain the aquatic plants and a liquid growth medium.

One variation of a method for monitoring growth of plants within a facility includes: aggregating global ambient data recorded by a suite of fixed sensors, arranged proximal a grow area within the facility, at a first frequency during a grow period; extracting interim outcomes of a set of plants, occupying a module in the grow area, from module-level images recorded by a mover at a second frequency less than the first frequency while interfacing with the module during the period of time; dispatching the mover to autonomously deliver the module to a transfer station; extracting interim outcomes of the set of plants from plant-level images recorded by the transfer station while sequentially transferring plants out of the module at the conclusion of the grow period; and deriving relationships between ambient conditions, interim outcomes, and final outcomes from a corpus of plant records associated with plants grown in the facility.

The moving object tracking apparatus emphasizes an image with specific size in each of fluoroscopic images derived from two or more paired fluoroscopic radiographic devices, obtains a value indicating certainty degree of detecting a candidate position of the object to be tracked on the image subjected to the emphasizing process, extracts the candidate position based on the value indicating the certainty degree of detection, calculates a value indicating a correlation between the candidate position extracted from images picked up from two or more directions, and a position of the fluoroscopic radiation generator, detects the position of the object to be tracked based on the value indicating the certainty degree of detection, and the value indicating the correlation, and controls irradiation of radiation to an irradiation target based on the detected position of the object to be tracked.

Systems and methods for use in identifying a brain condition of a subject are provided. In some aspects, a provided method includes constructing a classifier to identify a brain condition of a subject comprising steps of receiving image data obtained from a plurality of subjects, wherein the image data is acquired during an acquisition period following administration of at least one radioactive tracer. The method also includes defining a plurality of brain condition classes using the image data associated with one or more time frames during the acquisition period, and processing the image data to generate signatures corresponding to each of the plurality brain condition classes. The method further includes constructing the classifier using the signatures. The classifier can then be applied to determine a degree to which the subject expresses one or more disease states in order to determine a brain condition of the subject.