A method of detecting a protrusion in an intestinal tract in a computer according to an embodiment of the present disclosure includes acquiring a three-dimensional model of the intestinal tract scanned by a scanning device, the three-dimensional model comprising three-dimensional data of the intestinal tract; mapping, in the computer, the three-dimensional model to a two-dimensional plane in an area-preserving manner; and detecting an area of the protrusion in the two-dimensional plane. The method can replace traditional modes such as enteroscopy, and the protrusion in the intestinal tract is detected in a painless and low-cost mode.

A system and method include acquisition of a plurality of projection images of a subject, each of the projection images associated with a respective projection angle, determination, for each of the projection images, of a center-of-light location in a first image region, determination of a local fluctuation measure based on the determined center-of-light locations, and determination of a quality measure associated with the plurality of projection images based on the local fluctuation measure.

A candidate detection unit 118 detects, for each of a plurality of target images, candidate regions representing a specific detection target region using a discriminator. A region-label acquisition unit 120 acquires, for a part of the target images, position information of a search region as a teacher label. A region specifying unit 121 imparts, based on the part of the target images and the acquired position information of the search region, the position information of the search region to each of the target images, which are not the part of the target images, in semi-supervised learning processing. A filtering unit 122 outputs, for each of the acquired plurality of target images, among the candidate regions, a candidate region, an overlapping degree of which with the search region is equal to or larger than a fixed threshold.

A method and apparatus for enhancing magnetic resonance images to produce contrast-enhanced images without the need to administer contrast agent to a patient. The image processing apparatus utilises a trained machine learning algorithm as an image processor, preferably a generative adversarial network, to produce images from contrast agent-free magnetic resonance images with the produced images having similar appearance and better image quality and better pathological sensitivity and being able to differentiate more pathological conditions than actually acquired contrast-enhanced images.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a medical image. The processing circuitry is configured to calculate a first blood flow direction on the basis of a structure of a region of interest rendered in the medical image. The processing circuitry is configured to calculate a second blood flow direction on the basis of a structure in the surroundings of the region of interest. The processing circuitry is configured to identify a condition of the region of interest, on the basis of the first blood flow direction and the second blood flow direction.

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.

Augmenting real-time views of a patient with three-dimensional (3D) data. In one embodiment, a method may include identifying 3D data for a patient with the 3D data including an outer layer and multiple inner layers, determining virtual morphometric measurements of the outer layer from the 3D data, registering a real-time position of the outer layer of the patient in a 3D space, determining real-time morphometric measurements of the outer layer of the patient, automatically registering the position of the outer layer from the 3D data to align with the registered real-time position of the outer layer of the patient in the 3D space using the virtual morphometric measurements and using the real-time morphometric measurements, and displaying, in an augmented reality (AR) headset, one of the inner layers from the 3D data projected onto real-time views of the outer layer of the patient.

For correction of an image from an imaging system, an inverse solution uses an imaging prior as a regularizer and a physics model of the imaging system. An invertible network is used as the deep-learnt generative model in the regularizer of the inverse solution with the physics model of the degradation behavior of the imaging system. The prior model based on the invertible network provides a closed-form expression of the prior probability, resulting in a more versatile or accurate probability prediction.

Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.

The present disclosure relates to systems and methods for object recognition. The systems may obtain image data captured by an imaging device. The image data may include one or more objects. The systems may determine a centerline of a target object in the one or more objects based on the image data. The systems may determine a recognition result of the target object using a trained neural network model based on at least one feature parameter of the centerline of the target object. The recognition result may include a name of the target object. The systems may perform an anomaly detection on the target object based on the recognition result of the target object.