An information processing apparatus comprising at least one processor, wherein the at least one processor is configured to: acquire a document describing a subject; extract document finding information indicating a finding of the subject included in the document; and specify a finding extraction process for extracting image finding information indicating the finding indicated by the document finding information from a first image obtained by imaging the subject, among a plurality of types of finding extraction processes for extracting image finding information indicating a plurality of different types of findings that are able to be included in the first image.

An embodiment of the present disclosure provides a method of providing a User Interface for serial images analysis in a user equipment, the method including: displaying a first cross-sectional image, a second cross-sectional image, and a third cross-sectional image on a first area of the user interface, which are related to a first image; displaying candidate nodule information related to the first image on at least one of the first cross-sectional image, the second cross-sectional image, and the third cross-sectional image; determining the candidate nodule information related to a user input as first nodule information related to the first image, based on the user input on the user interface; and displaying the first nodule information in such a way that the candidate nodule information related to the user input is replaced with the first nodule information, in which the candidate nodule information may be generated based on a first nodule dataset obtained by inputting the first image to a deep learning algorithm in a server.

A system and method for improved image acquisition of multiple pulsed X-ray source-in-motion tomosynthesis imaging apparatus by generating the electrocardiogram (ECG) waveform data using an ECG device. Once a representative cardiac cycle is determined, system will acquire images only at rest period of heart beat. Real time ECG waveform is used as ECG synchronization for image improvement. The imaging apparatus avoids ECG peak pulse for better chest, lung and breast imaging under influence of cardiac periodical motion. As a result, smoother data acquisition, much higher data quality can be achieved. The multiple pulsed X-ray source-in-motion tomosynthesis machine is with distributed multiple X-ray sources that is spanned at wide scan angle. At rest period of one heartbeat, multiple X-ray exposures are acquired from X-ray sources at different angles. The machine itself has capability to acquire as many as 60 actual projection images within about two seconds.

A CADx system for analysing medical images to monitor at least one feature on the image to predict at least one of a change of the state or maintenance of the current state of a monitored feature within a time frame is described. The system comprising: an input circuit for receiving at least one medical input image; a feature state change circuit for analysing the received input image and predicting a state change comprising: a state change predictor to predict a state change of the monitored feature within the time frame; and an output circuit to output an indication of the change of state or maintenance of the current state of the monitored feature within the time frame based on the prediction of the feature state change predictor. A method of training a feature state change prediction circuit using Machine Learning is also described.

A method includes positioning a patient at a first orientation relative to a radiation source. The method further includes using a 3D imaging technique to measure one or more positions of the patient's chest. The method further includes, while using the 3D imaging technique to measure the one or more positions of the patient's chest: generating a model of the patient's chest using the one or more positions of the patient's chest; updating the model of the patient's chest as the patient breathes; and exposing the patient to a dose of radiation using the radiation source, wherein the dose is based on the model of the patient's chest.

A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

The present disclosure relates to computer-implemented systems and methods for detecting a feature-of-interest in a video. In one implementation, a computer-implemented system may include a discriminator network and a generative network. The discriminator network may include a perception branch and an adversarial branch, the perception branch being configured to output detections of the feature-of-interest in the video. The generative network may be configured to receive detections of the feature-of-interest from the perception branch of the discriminator network and generate artificial representations of the feature-of-interest based on the detections from the perception branch. Further, the adversarial branch may be configured to provide an output identifying differences between the false representations and true representations of the feature-of-interest, and the perception branch may be further configured to be trained by the output of the adversarial branch so that false representations are not detected by the perception branch as true representations.

Systems and methods for generating synthesized medical images for training a machine learning based network are provided. An input medical image in a first modality is received. The input medical image comprises a nodule region for each of one or more nodules and a remaining region. The input medical image comprises an annotation for each of the one or more nodules. A synthesized medical image in a second modality is generated from the input medical image. The synthesized medical image comprises the annotation for each of the one or more nodules. A synthesized nodule image of each of the nodule regions and synthesized remaining image of the remaining region are generated in the second modality. It is determined whether each particular nodule of the one or more nodules is visible in the synthesized medical image based on at least one of the synthesized nodule image for the particular nodule and the synthesized remaining image. In response to determining that at least one nodule of the one or more nodules is not visible in the synthesized medical image, the annotation for the at least one not visible nodule is removed from the synthesized nodule image.

Described herein are means for training a deep model to learn contrastive representations embedded within part-whole semantics via a self-supervised learning framework, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specifically configured for performing a random cropping operation to crop a 3D cube from each of a plurality of medical images received at the system as input, performing a resize operation of the cropped 3D cubes, performing an image reconstruction operation of the resized and cropped 3D cubes to predict the resized whole image represented by the original medical images received; and generating a reconstructed image which is analyzed for reconstruction loss against the original image representing a known ground truth image to the reconstruction loss function. Other related embodiments are disclosed.

A method includes receiving a time series of slice images of medical imaging. The images have a region of interest located at a lung lesion. The method also includes tracking over at least one subset of slice images in a time series of slice images variations over time of at least one image parameter at the set of points in the region of interest. Classifier processing is applied to set of signals indicative of tracked time variations of the at least one image parameter at respective points in the set of points. A classification signal is indicative of the tracked time variations of the at least one image parameter reaching or failing to reach at least one classification threshold.