Provided is an image processing apparatus configured to process an image of a fundus of an eye to accurately measure thicknesses of membranes that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points; and a measuring position acquiring unit configured to identify a measuring position regarding the wall of the blood vessel based on a position of the identified cell.

Systems and methods are provided for segmenting tissue within a computed tomography (CT) scan of a region of interest into one of a plurality of tissue classes. A plurality of atlases are registered to the CT scan to produce a plurality of registered atlases. A context model representing respective likelihoods that each voxel of the CT scan is a member of each of the plurality of tissue classes is determined from the CT scan and a set of associated training data. A proper subset of the plurality of registered at lases is selected according to the context model and the registered atlases. The selected proper subset of registered atlases are fused to produce a combined segmentation.

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.

Robust calcification tracking is provided in fluoroscopic imagery. A patient with an inserted catheter is scanned over time. A processor detects the catheter in the patient from the scanned image data. The processor tracks the movement of the catheter. The processor also detects a structure represented in the data. The structure is detected as a function of movement with a catheter. The processor tracks the movement of the structure using sampling based on a previous location of the structure in the patient. The processor may output an image of the structure.

Provided is an image processing apparatus configured to process an image of a fundus of an eye, which is capable of simply and accurately measuring a distribution of cells that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; and a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points.

Provided is an image processing device including a matching unit that performs matching processing between a predetermined pattern on a surface of a 3D model of a biological tissue including an operating site generated on the basis of a preoperative diagnosis image and a predetermined pattern on a surface of the biological tissue included in a captured image during surgery, a shift amount estimation unit that estimates an amount of deformation from a preoperative state of the biological tissue on the basis of a result of the matching processing and information regarding a three-dimensional position of a photographing region which is a region photographed during surgery on the surface of the biological tissue, and a 3D model update unit that updates the 3D model generated before surgery on the basis of the estimated amount of deformation of the biological tissue.

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

A photoacoustic image evaluation apparatus includes a processor configured to acquire a first photoacoustic image generated at a first point in time and a second photoacoustic image generated at a second point in time before the first point in time, the first and second photoacoustic images being photoacoustic images generated by detecting photoacoustic waves generated inside a subject, who has been subjected to blood vessel regeneration treatment, by emission of light into the subject; acquire a blood vessel regeneration index, which indicates a state of a blood vessel by the regeneration treatment, based on a difference between a blood vessel included in the first photoacoustic image and a blood vessel included in the second photoacoustic image; and display the blood vessel regeneration index on a display.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

A method for identifying candidate target cells within a biological fluid specimen includes a digital image of the biological fluid specimen with the digital image having a plurality of color channels, identifying first connected regions of pixels of a minimum first intensity in a first channel, identifying second connected regions of pixels of a minimum second intensity in a second channel, and determining first connected regions and second connected regions that spatially overlap. For a pair of a first connected region and a second connected region that spatially overlap, whether the second connected region overlaps the first connected region by a threshold amount is determined, and if the second connected region overlaps the first connected region by the threshold amount then the portion of the image corresponding to the overlap is continued to be treated as a candidate for classification.