The current document is directed to methods and systems for monitoring a dental patient's progress during a course of treatment. A three-dimensional model of the expected positions of the patient's teeth can be projected, in time, from a three-dimensional model of the patient's teeth prepared prior to beginning the treatment. A digital camera is used to take one or more two-dimensional photographs of the patient's teeth, which are input to a monitoring system. The monitoring system determines virtual-camera parameters for each two-dimensional input image with respect to the time-projected three-dimensional model, uses the determined virtual-camera parameters to generate two-dimensional images from the three-dimensional model, and then compares each input photograph to the corresponding generated two-dimensional image in order to determine how closely the three-dimensional arrangement of the patient's teeth corresponds to the time-projected three-dimensional arrangement.

An information processing apparatus includes an image acquisition unit, a correspondence relation acquisition unit, an image transformation unit, a change calculation unit, and a statistics amount calculation unit. The image acquisition unit acquires a first image and a second image. The correspondence relation acquisition unit acquires a spatial correspondence relation between the first and the second images. The image transformation unit acquires a transformed image by transforming the second image to substantially coincide with the first image based on the spatial correspondence relation. The change calculation unit calculates a volume or area change between the transformed image and the second image based on the spatial correspondence relation. The statistics amount calculation unit calculates a statistics amount for pixel values of the transformed image based on the volume or area change.

A system and method is provided for imaging a contrast agent. The system includes a power injector that delivers a contrast agent as a series of boluses using a known period, flow rate, or duration and with a rate of at least one or more separate boluses per cardiac cycle. An x-ray imaging system acquires a reference dataset of the subject before the contrast agent is delivered and acquires an imaging dataset as the series of boluses are delivered to the subject, wherein multiple images are acquired of the subject per bolus. A computer system receives the reference dataset and the imaging dataset from the x-ray imaging system and reconstructs the reference dataset and the imaging dataset using a reconstruction process that removes the subject from the images to generate time-resolved volumetric images of the contrast agent moving within a volume of the subject without the subject.

An image processing apparatus includes processing circuitry configured: to obtain a plurality of images taken so as to include a target site of a subject in temporal phases; and to calculate an index indicating a state of an adhesion at a boundary between a first site of the subject corresponding to a first region and a second site of the subject corresponding to a second region, by using classification information used for classifying each of pixels into one selected from between a first class related to the first region and a second class related to a second region positioned adjacent to the first region in a predetermined direction, on a basis of mobility information among the images in the temporal phases with respect to the pixels in the images that are arranged in the predetermined direction across the boundary between the first region and the second region of the images.

Disclosed herein are methods for predicting the reprogramming process of cells from a microscopic image of one or more cells. According to some embodiments, the method includes capturing an image of region of interest (ROI) of every pixel of the microscopic image, followed by processing the ROI image with a trained convolutional neural network (CNN) model and a trained long short-term memory (LSTM) network so as to obtain predicted probability maps. Also disclosed herein are a storage medium and a system for executing the present methods.

A method of assessing spinal column stability involves receiving image data corresponding to a spinal column of a patient; determining, based on the image data, a material strength of bony anatomy in at least a portion of the spinal column; completing a first stability assessment of the spinal column, based at least in part on the determined material strength; modifying the image data to simulate removal of bony anatomy or soft tissue from the spinal column to yield modified image data; and completing a second stability assessment of the spinal column, based at least in part on the determined material strength and the modified image data.

A system for deforming and analyzing a plurality of particles carried in a sample volume includes a substrate defining an inlet, configured to receive the sample volume, and an outlet; and a fluidic pathway fluidly coupled to the inlet and the outlet. The fluidic pathway includes a delivery region configured to receive the plurality of particles from the inlet and focus the plurality of particles from a random distribution to a focused state, a deformation region defining an intersection located downstream of the delivery region and coupled to the outlet, and wherein the deformation region is configured to receive the plurality of particles from the delivery region and to transmit each particle in the plurality of particles into the intersection from a single direction, a first branch fluidly coupled to the deformation region and configured to transmit a first flow into the intersection, and a second branch fluidly coupled to the deformation region and configured to transmit a second flow, substantially opposing the first flow, into the intersection, wherein the first flow and the second flow are configured to induce extension of one or more particles in the plurality of particles.

The present disclosure may provide a method for perfusion analysis. The method may include: obtaining a plurality of scan images corresponding to a plurality of time points; obtaining a plurality of time-density discrete points based on the plurality of scan images; determining an initial time-density curve based on the plurality of time-density discrete points, the initial time-density curve indicating a density variation of a contrast agent in an organ or tissue over time, the organ or tissue corresponding to a pixel or voxel in the plurality of scan images; obtaining a first perfusion model; determining a first perfusion parameter based on the first perfusion model and the initial time-density curve; obtaining a second perfusion model; and determining a second perfusion parameter based on the second perfusion model and the first perfusion parameter.

A stress prediction device for predicting mechanical stress exerted to a deformable object due contact between the object and an external device that is to be inserted into the object at an intended insertion position comprises a segmentation unit configured to access generic model data representing a generic reference object that comprises predefined secondary landmark features at predefined landmark positions, which are not identifiable using a predefined imaging technique, and pre-insertion object image data acquired using the imaging technique. It provides segmented object model data which comprises associated mapped landmark position data indicative of mapped landmark positions of the secondary landmark features. A stress determination unit determines and provides predictive stress information indicative of mechanical stress exerted to at least one of the secondary landmark features at the associated mapped landmark position due to mechanical contact between the object and the external device.

A method and system is described that includes obtaining a whole blood sample and obtaining a first light absorbance profile of the whole blood sample. Next, the whole blood sample is hemolyzed to generate a hemolyzed sample of blood and a second light absorbance profile of the hemolyzed sample of blood is obtained. The level of hemolysis in the whole blood sample is determined by comparing the first light absorbance profile and the second light absorbance profile.