A method of displaying an area of interest within a surgical site includes modeling a patient's lungs and identifying a location of an area of interest within the model of the patient's lungs. The topography of the surface of the patient's lungs is determined using an endoscope having a first camera, a light source, and a structured light pattern source. Real-time images of the patient's lungs are displayed on a monitor and the real-time images are registered to the model of the patient's lungs using the determined topography of the patient's lungs. A marker indicative of the location of the area of interest is superimposed over the real-time images of the patient's lungs. If the marker falls outside of the field-of view of the endoscope, an arrow is superimposed over the real-time images to indicate the direction in which the marker is located relative to the field of view.

A method for lung nodule evaluation is provided. The method may include obtaining a target image including at least a portion of a lung of a subject. The method may also include segmenting, from the target image, at least one target region each of which corresponds to a lung nodule of the subject. The method may further include generating an evaluation result with respect to the at least one lung nodule based on the at least one target region.

A radiation tomography system includes a radiation tomography apparatus and an image processing apparatus. The image processing apparatus includes an image reconstruction unit, an attenuation distribution image creation unit, and an attenuation correction unit. The attenuation distribution image creation unit includes a first processing unit and a second processing unit. The first processing unit creates and outputs an intermediate image based on an emission scan image using a trained neural network. The second processing unit creates and outputs an attenuation distribution image based on the intermediate image.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

A scanner system for capturing a three-dimensional model of an object includes a laser and a camera to capture two-dimensional images of the object. The system also includes a tube mounted to a rail, a central processor configured to receive data collected from the laser and the camera and an actuation mechanism configured to move the tube along the rail. The tube is configured to move generally along a travel axis of the rail. The tube includes open first and second tube ends. The laser and camera are mounted inside the tube between the first and second tube ends. The first tube end includes a first continuous ring and the second tube end includes a second continuous ring. A channel extends through the tube between the first and second rings positioned adjacent the rail in an assembled configuration.

The present disclosure provides a system comprising a SPECT or PET device; a CT device; and a computer comprising memory and a processor in communication with the memory, the memory comprising a computer application program for a method of performing dosimetric analysis of an organ. The computer application program is executable by the processor to perform the method. The method comprising receiving single photon emission computed tomography (SPECT) or positron emission tomography (PET) images at time instances, the SPECT or PET images relating to the organ. The method then receives a computed tomography (CT) image at one of the time instances, the CT image relating to the organ. Virtual CT images are then generated at the other time instances based on the received SPECT or PET images and the CT image. An absorbed dose of ionising radiation on the organ can then be measured based on the received SPECT or PET images, the received CT image, and the generated virtual CT images. The method generates the virtual CT images using any one of: SPECT to SPECT (or PET to PET) registration, CT to SPECT (PET) registration, and SPECT (PET) to CT registration.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

The system and method of the invention pertains to automated analysis and reconstruction of images from a plurality of imaging devices to determine the presence of different types of artifacts, using signal processing and machine learning algorithms. The method (1) classifies the artifacts according to their cause, (2) selects correction algorithms to address the artifact, or artifact-generating data, and (3) selects the data or sections of the data and/or reconstruction parameters to be corrected. Then, another reconstruction is performed with the selected artifact corrections, yielding a second reconstructed image with less artifact content. The process can be applied iteratively until the artifact content of the reconstructed image is reduced to a satisfactory low level as determined by a user. If the artifacts cannot be addressed by data processing means, the method initiates or recommends alternative artifact management actions.

Systems and methods are disclosed for anatomic structure segmentation in image analysis, using a computer system. One method includes: receiving an annotation and a plurality of keypoints for an anatomic structure in one or more images; computing distances from the plurality of keypoints to a boundary of the anatomic structure; training a model, using data in the one or more images and the computed distances, for predicting a boundary in the anatomic structure in an image of a patient's anatomy; receiving the image of the patient's anatomy including the anatomic structure; estimating a segmentation boundary in the anatomic structure in the image of the patient's anatomy; and predicting, using the trained model, a boundary location in the anatomic structure in the image of the patient's anatomy by generating a regression of distances from keypoints in the anatomic structure in the image of the patient's anatomy to the estimated boundary.