The present invention proposes an ultrasound system and a method of detecting lung sliding on the basis of a temporal sequence of ultrasound data frames of a first region of interest. The first region of interest includes a pleural interface of a lung. A sub-region identifier (410) is configured to identify, for each of the ultrasound data frames, a sub-region of a scanned region of the ultrasound data frame, the sub-region comprising at least part of the pleural interface; a lung sliding detector (420) is configured to derive a parametric map for the sub-region on the basis of at least two ultrasound data frames of the temporal sequence, parametric values of the parametric map indicating a degree of tissue motion over the at least two ultrasound frames; wherein the lung sliding detector is further configured to extract data of the sub-regions from the at least two ultrasound data frames, and to derive the parametric map on the basis of the extracted data.

The present invention relates to the technical field of medical treatment, in particular to an image recognition method and device based on a deep convolutional neural network. The method comprises the following steps: pre-processing chest X-ray films to obtain initial X-ray film images that meets format requirements; screening the initial X-ray film images to detect whether they are posteroanterior chest images; inputting the posteroanterior chest images into a binary classification model of the deep convolutional neural network for negative and positive classification; inputting the images presenting positive results into a detection model of the deep convolutional neural network to detect a disease type and label an outline of a lesion area in each image; and displaying the disease type and lesion area corresponding to the image. According to the image recognition method based on the deep convolutional neural network provided by this embodiment of the present invention, whether the chest X-ray films are negative or positive can be screened, the lesion areas can also be positioned, and meanwhile, the types or signs of the diseases in the lesion areas can be labeled to provide doctors with more interpretable reference opinions.

Disclosed is a method for analyzing a lesion based on a medical image, which is performed by a computing device. The method may include: obtaining positional information of a suspicious nodule which exists in the medical image; generating a mask for the suspicious nodule based on a patch of the medical image corresponding to the positional information; and determining a class for a state of the suspicious nodule based on the patch of the medical image and the mask for the suspicious nodule.

A report generating system is operable to generate inference data for a medical scan indicating a first subset of a plurality of anatomical features of the medical scan are normal. A set of default natural language text corresponding to the first subset of the plurality of anatomical features are identified based on report template data. Preliminary report data is generated to include the set of default natural language text corresponding to the first subset of the plurality of anatomical features based on the inference data. The preliminary report data is displayed an interactive user interface, and review data is received based on user input in response to at least one prompt displayed via the interactive user interface. Final report data that includes natural language text data for each of the plurality of report sections is generated based on the review data.

The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.

A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented. To detect nodules in an imaging scan, a 3D CNN using a single feed forward pass of a single network is used. After detection, risk stratification is performed using a supervised or an unsupervised deep learning method to assist in characterizing the detected nodule/neoplasm as benign or malignant. The supervised learning method relies on a 3D CNN used with transfer learning and a graph regularized sparse MTL to determine malignancy. The unsupervised learning method uses clustering to generate labels after which label proportions are used with a novel algorithm to classify malignancy. The method assists radiologists in improving detection rates of lung nodules to facilitate early detection and minimizing errors in diagnosis.

The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.

One or more example embodiments describes a method of performing lung nodule assessment, which method comprises the steps of obtaining a lung scan for a patient from an imaging modality; obtaining a blood panel for that patient from a blood analysis modality; and processing the lung scan and the blood panel in a classifier, which classifier is trained to assess a lung nodule based on the lung scan and the blood panel. The invention further describes a method of training such a classifier, and a lung nodule assessment arrangement.

An X-ray imaging system using multiple pulsed X-ray sources to perform highly efficient and ultrafast 3D radiography is presented. There are multiple pulsed 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.

A method for analyzing a patient based on a volumetric pulmonary scan includes receiving volumetric pulmonary scan data representative of a patient's pulmonary structure. This method also includes determining a level of transpulmonary pressure defining an effort metric based on one or more characteristics from the received volumetric pulmonary scan data. This method further includes determining one or more physiological or anatomical parameters associated with the transpulmonary pressure based on the received volumetric pulmonary scan data and the effort metric. A non-transitory computer readable medium can be programmed with instructions for causing one or more processors to perform the method for analyzing a patient based on a volumetric pulmonary scan.