In one embodiment, a diagnosis support apparatus includes: an input circuit configured to acquire a first medical image; and processing circuitry configured to generate a second medical image from the first medical image in such a manner that information included in the second medical image is reduced from information included in the first medical image, extract auxiliary information from the first medical image, and perform inference of a disease by using the second medical image and the auxiliary information.

A method for generating a machine learning model for characterizing a plurality of Regions Of Interest ROIs based on a plurality of 3D medical images and an associated method for characterizing a Region Of Interest ROI based on at least one 3D medical image. The methods proposed here aim to provide complementary strategies to enable a classification of ROIs from 3D medical images which could take profit of the advantageous and complementarity of both 2D and 3D CNNs to improve the accuracy of the prediction. More precisely, the present disclosure proposes a 2D model that complements the 3D model so that the sensitivity/specificity of the diagnosis is improved by taking advantage of complementary notions.

In one or more implementations, systems, methods and computer implemented processes are provided that are directed to a method of treating a subject with a lung tumor, the method comprising: obtaining computed tomography (CT) image slices of the subject, wherein the CT image slices comprise images of the lung tumor. In a further implementation, the systems, methods and computer implemented processes are directed to identifying a first CT image slice where the lung tumor has a largest diameter among the CT image slices; and determining intensity-skewness of the lung tumor on the first CT image slice. In a further implementation, the systems, methods and computer implemented processes are directed to treating the subject with surgery, chemotherapy and/or radiotherapy, if the intensity-skewness is no greater than -1.5.

An information processing apparatus includes an image feature acquiring unit configured to acquire first image features and second image features from a medical image and a deriving unit configured to derive image findings of a plurality of items belonging to a first finding type based on the first image features and deriving image findings of a plurality of items belonging to a second finding type different from the first finding type based on the second image features that at least partly differ from the first image features.

An X-ray imaging system with multiple pulsed X-ray source tubes in motion to perform highly efficient and ultrafast 3D radiography is presented. There are multiple X-ray tubes from pulsed sources mounted on a structure in motion to form an array of X-ray tubes. The tubes move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Each individual X-ray tube in each individual source can also move rapidly around its static position in a small distance. When a tube has a speed that is equal to group speed but with opposite moving direction, the tube and X-ray flat panel detector are activated through an external exposure control unit so that the tube stay momentarily standstill. It results in much reduced travel distance for each X-ray source tube and much lighter load for motion system. 3D X-ray scan can cover much wider sweeping angle in much shorter time and image analysis can also be done in real time.

A method of securely accessing an image review unit, including: a triage unit configured to determine if an image of interest is normal or abnormal based upon a reference image and extract normal features from the image of interest based on normal features indicated in the reference image, wherein the reference image and the image of interest are acquired by a same medical imaging device or same doctor or same medical facility; and an image transformation unit configured to reconstruct the image of interest based upon the reference image so as to align the normal features in the image of interest with the normal features in the reference image.

One or more implementations allow for detecting B-lines in ultrasound video and images for diagnostic purposes through analysis of Q-mode images for B-line detection.

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.

An exemplary processing system accesses a three-dimensional (3D) model of an anatomical structure of a patient and applies a detection process to the 3D model to detect a single-layer anatomical feature in the anatomical structure. The detection process includes generating, from the 3D model, a probability map of candidate points for the single-layer anatomical feature, and generating, based on the probability map of candidate points, a single-layer mesh representing the single-layer anatomical feature.

A device may obtain first information relating to one or more first lung nodules identified in first imaging of a chest of a patient and second information relating to one or more second lung nodules identified in second imaging of the chest of the patient. The device may provide the first information and the second information to a machine learning model. The device may determine, using the machine learning model, a risk of lung cancer associated with the patient based on an elapsed time between performance of the first imaging and the second imaging and differences between the first information and the second information. The risk of lung cancer may have an inverse correlation to the elapsed time and a direct correlation to the differences. The device may perform one or more actions based on the risk of lung cancer that is determined