A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.

Systems and methods for detecting aortic aneurysms using ensemble based deep learning techniques that utilize numerous computed tomography (CT) scans collected from numerous de-identified patients in a database. The system includes software that automates the analysis of a series of CT scans as input (in DICOM file format) and provides output in two dimensions: (1) ranking CT scans by risks of adverse events from aortic aneurysm, (2) providing aortic aneurysm size estimates. A repository of CT scans may be used for training of deep neural networks and additional data may be drawn from localized patient information from institutions and hospitals which grant permission.

A method performed by a mobile device for handling identification of equipment. The mobile device records an image, in a recording direction at a first location, of the equipment. Upon recording the image, the mobile device further obtains one or more radiation indications for determining a direction of radiation from the equipment; and provides the obtained one or more radiation indications associated with the recorded image, to an internal identifying process at the mobile device and/or a network node for identifying the equipment.

A system (100) includes a computer readable storage medium (122) with computer executable instructions (124), including: a biophysical simulator component (126) configured to determine a fractional flow reserve value via simulation and a traffic light engine (128) configured to track a user-interaction with the computing system at one or more points of the simulation to determine the fractional flow reserve value. A processor (120) is configured to execute the biophysical simulator component to determine the fractional flow reserve value and configured to execute the traffic light engine to track the user-interaction with respect to determining the fractional flow reserve value and provide a warning in response to determining there is a potential incorrect interaction. A display is configured to display the warning requesting verification to proceed with the simulation from the point, wherein the simulation is resumed only in response to the processor receiving the requested verification.

A medical system includes: an endoscope and at least one treatment tool; a treatment-tool coordinate calculating unit that extracts the treatment tool by processing two or more images acquired at different times by the endoscope, that determines directions of longitudinal axes of the extracted treatment tool, and that calculates a coordinate of an intersection of the determined two or more longitudinal axes; and a judgment unit that judges whether the treatment tool serves as a follow target, on the basis of the coordinate of the intersection calculated by the treatment-tool coordinate calculating unit.

A system is configured to perform operations includes accessing a set of model points of a model of an anatomic structure of a patient, the model points being associated with a model space. A set of measured points of the anatomic structure of the patient are collected, the measured points being associated with a patient space. The set of model points are registered to the set of measured points using a first set of initial parameters to generate a first transformation. One or more sets of perturbed initial parameters are generated based on the first set of initial parameters. One or more perturbed registration processes are performed to register the set of model points to the set of measured points using the one or more sets of perturbed initial parameters respectively to generate corresponding perturbed transformations. A registration quality indicator is generated based on the first transformation and the one or more perturbed transformations.

A bronchial stent includes a first branch configured to widen, open, and/or mechanically support a first airway; an obstructive portion that, when the stent is deployed in the first airway, obstructs a second airway, the second airway forming a branching connection with the first airway; and a feature proximal to the obstructive portion, the feature configured to facilitate opening of the obstructive portion.

This application disclosures a method and system for detecting a centerline of a vessel. The method may include obtaining image data, wherein the image data may include vessel data; selecting two endpoints of the vessel based on the vessel data; transforming the image data to generate a transformed image based on at least one image transformation function; and determining a path of the centerline of the vessel connecting the first endpoint of the vessel and the second endpoint of the vessel to obtain the centerline of the vessel based on the transformed image. The two endpoints of the vessel may include a first endpoint of the vessel and a second endpoint of the vessel.

A medical image processing apparatus according to an embodiment includes processing circuitry configured: to generate a projection image by implementing an intensity projection on a plurality of two-dimensional images structuring three-dimensional volume data rendering a tubular organ; to obtain a mapping matrix of the intensity projection; to annotate the tubular organ in the projection image; and to identify the tubular organ in the three-dimensional volume data, by inversely mapping the tubular organ annotated in the projection image onto the three-dimensional volume data while using the mapping matrix.

Automation of microscopic pathological diagnosis relies on digital image quality, which, in turn, affects the rates of false positive and negative cellular objects designated as abnormalities. Cytogenetic biodosimetry is a genotoxic assay that detects dicentric chromosomes (DCs) arising from exposure to ionizing radiation. The frequency of DCs is related to radiation dose received, so the inferred radiation dose depends on the accuracy of DC detection. To improve this accuracy, image segmentation methods are used to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data in a sample based on novel quality measures. When sufficient numbers of high quality images are found, the microscope system is directed to terminate metaphase image collection for a sample. The International Atomic Energy Agency recommends at least 500 images be used to estimate radiation dose, however often many more images are collected in order to select the metaphase cells with good morphology for analysis. Improvements in DC recognition increase the accuracy of dose estimates, by reducing false positive (FP) DC detection. A set of chromosome morphology segmentation methods selectively filtered out false DCs, arising primarily from extended prometaphase chromosomes, sister chromatid separation and chromosome fragmentation. This reduced FPs by 55% and was highly specific to the abnormal structures (≥97.7%). Additional procedures were then developed to fully automate image review, resulting in 6 image-level filters that, when combined, selectively remove images with consistently unparsable or incorrectly segmented chromosome morphologies. Overall, these filters can eliminate half of the FPs detected by manual image review. Optimal image selection and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Consequently, the average dose estimation error was reduced from 0.4 Gy to <0.2 Gy with minimal manual review required. Automated image selection with these filters reduces the number of images that are required to capture metaphase cells, thus decreasing the number of images and time required for each sample. A microscope system integrates image selection procedures controls with an automated digitally controlled microscope then determines at what point a sufficient number of metaphase cell images have been acquired to accurately determine radiation dose, which then terminates data collection by the microscope. These image filtering approaches constitute a reliable and scalable solution that results in more accurate and rapid radiation dose es