The disclosed image acquisition medical device and medical system make it possible to easily grasp an orientation of a distal end portion of the medical device based on an angiographic image and a tomographic image. The image acquisition medical device includes a flexible body portion that extends in an axial direction; an image sensor that is disposed in the body portion and that is configured to acquire an image of a hollow organ; and a contrast unit that protrudes toward a distal end side of the body portion and that makes an orientation of a distal end portion of the body portion visually recognizable in an angiographic image. Relative positions of the image sensor and the contrast unit in an axial rotation direction are fixed.

The present application discloses a method, an apparatus, a storage medium and a processor for medical image auto-segmentation. The method includes: acquiring a medical image scan of the day of a target patient, wherein the medical image scan of the day is a medical image scanned before the Nth time treatment of the target patient, and N is a natural number greater than 1; performing auto-segmentation on the medical image scan of the day by using a target model to obtain a segmentation result, wherein the target model is a model generated after iterative training based on all images along with the contours from previous N times fractions of the target patient, segmentation result is used to generate the contour for the target treatment plan; and generating the contour for the target treatment plan based on a segmentation result.

The current disclosure provides methods and systems for visualizing, comparing, and navigating through, labeled image sequences. In one example, a degree of variation between a plurality of labels for an image in a sequence of images may be encoded as a comparison metric, and the comparison metric for each image may be graphed as a function of image position in the sequence of images, thereby providing a contextually rich view of label variation as a function of progression through the sequence of images. Further, the encoded variation of image labels may be used to automatically flag inconsistently labeled images, wherein the flagged images may be highlighted in a graphical user interface presented to a user, pruned from a training dataset, or a loss associated with the flagged image may be scaled based on the encoded variation during training of a machine learning model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

A method and system are disclosed for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols. The method includes the steps of: providing a plurality of radiographic images, detecting and segmenting in each of the radiographic image at least one regions of interest (ROI) as reference ROI, measuring at least one radiomic feature per reference ROI, identifying valid reference ROIs based on the measured radiomics values, and clustering the measured radiomics values of valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition. A method and system are disclosed for classifying radiographic images by applying a machine learning model generated for automatic classification of radiographic images.

Aspects of the present disclosure relate to systems, devices and methods for performing a surgical step or surgical procedure for example with visual guidance using a head mounted display or with a surgical navigation system or with a surgical robot. A computer processor can be configured to determine the pose of a first vertebra with an attached first marker and a second vertebra with an attached second marker. The computer processor can be configured to determine the pose of at least one vertebra interposed or adjacent to the first and second vertebrae with attached markers, e.g. fiducial markers.

Provided are an ultrasound imaging apparatus and an operation method for registering an ultrasound image and an image from another modality. The ultrasound imaging apparatus may register the ultrasound image and the image from the other modality based on a three-dimensional positional relationship between at least one external electromagnetic sensor attached to a patient's body and an ultrasound probe and on a position of a feature point extracted from the image from the other modality.

A method performed by a computing system comprises receiving a fluoroscopic image of a patient anatomy while a portion of a medical instrument is positioned within the patient anatomy. The fluoroscopic image has a fluoroscopic frame of reference. The portion has a sensed position in an anatomic model frame of reference. The method further comprises identifying the portion in the fluoroscopic image and identifying an extracted position of the portion in the fluoroscopic frame of reference using the identified portion in the fluoroscopic image. The method further comprises registering the fluoroscopic frame of reference to the anatomic model frame of reference based on the sensed position of the portion and the extracted position of the portion.

Risk prediction models are trained and deployed to analyze images, such as computed tomography scans, for predicting future risk of lung cancer for one or more subjects. Individual risk prediction models are separately trained on nodule-specific and non-nodule specific features such that each risk prediction model can predict future risk of lung cancer across different time periods (e.g., 1 year, 3 years, or 5 years). Such risk prediction models are useful for developing preventive therapies for lung cancer by enabling clinical trial enrichment.

The present invention relates to a method for acquiring tomographic images of a sample in a microscopy system, wherein the sample comprises a defined region, and wherein the method comprises determining a location in three-dimensional space of the defined region, wherein the method further comprises capturing an image of at least a part of the sample, and wherein the determination of the location in three-dimensional space of the defined region is based, at least in part, on the image of the part of the sample. The present invention also relates to a corresponding microscopy system and a computer program product to perform the method according to the present invention.