A video processor includes: a region extracting circuit that receives an input of a first image signal obtained by forming an image of a subject irradiated with first narrow band light including a wavelength that is minimally absorbed by blood within a green wavelength band, the image of the subject including a bleeding point, and extract a blood pool region having a blood concentration that is lower than a blood concentration of the bleeding point in a region representing the blood in the first image signal; and an image generating circuit that raises a luminance value of the blood pool region in either the first image signal or a second image signal obtained by forming an image of the subject irradiated with second narrow band light whose wavelength is shorter and which is more absorbed by the blood than the first narrow band light.

Computed tomography perfusion (CTP) is used in a method to identify cancerous lesions having genetic mutations and treat them accordingly. Also, CTP values are used to distinguish primary versus metastatic lesions. For example, pulmonary blood flow is identified as one biomarker for EGFR and KRAS genetic mutations in lung cancer, lesion having dual-input pulmonary blood flow exceeding a threshold (e.g., 103 ml/min/100 mL with sensitivity 100% and specificity 62%) are determined as having mutations. The CTP values are calculated using a lesion region-of-interest (ROI) placed to include the area of maximum perfusion intensity within the lesion bass and surrounding blush, while avoiding regions of perfusion inhomogeneity (e.g., due to necrosis). In certain implementations, instead of a binary determination, the method can generate probabilities associated with respective alternatives (e.g., mutation/non-nutation and/or primary/secondary), and the method can use multivariable statistical analysis that incorporates patient and/or medical information in addition to CTP values.

A system and method for assessing blood flow include: an ocular lens; a light source; a digital video camera; a biosensor; a trigger; and a computer. The ocular lens is for viewing a fundus of an eye. The light source is for illuminating the fundus. The digital video camera is for imaging the fundus. The biosensor is for sensing a pulse waveform. The computer is configured for: recording input frames and pulse waveform data in response to an input from the trigger; defining a low-pass frequency and a high-pass frequency from the pulse waveform data; stabilizing the input frames; enhancing contrast of the input frames; separating the input frames into sub-channels; conducting eulerian video magnification for color amplification using the inputs of image sampling rate, the low-pass frequency, the high-pass frequency, and an amplification factor; reconstructing the sub-channels into output frames; and combining the output frames with the input frames.

The disclosure provides a method and system for determining a lumen volume of a target blood vessel. The method may include acquiring a temporal sequence of angiography images of the target blood vessel after a contract agent is injected in the target blood vessel. The method may further include identifying a region of interest containing the target blood vessel, by a processor, in each angiography image in the temporal sequence of angiography images. The method may also include integrating, by the processor, pixel values in each region of interest, and determining the lumen volume, by the processor, based on the integrated values of the regions of interest and a predetermined correlation between the integrated values and volumes of the contrast agent.

A biological information acquisition device includes a near-infrared light source, a solid-state image sensor, and an information processing device, wherein the solid-state image sensor includes at least one specific pixel having a sensitivity peak in a specific wavelength band of 620 nm or more and 1100 nm or less, the information processing device is configured to repeatedly acquire (i) an emission image frame captured by the solid-state image sensor during an emission period of the near-infrared light source and (ii) a non-emission image frame captured by the solid-state image sensor during a non-emission period of the near-infrared light source, and the information processing device is configured to derive biological information on the basis of the emission image frame and the non-emission image frame.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

In some embodiments an apparatus for monitoring a physiological parameter is provided. The apparatus includes a housing configured to be placed into an ear canal of a subject. The apparatus further includes a sensor configured to measure at least one of blood vessel anatomy, a blood vessel dimension, blood flow properties, or a presence of a molecule in a blood vessel.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

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.

An actual measurement value calculation unit calculates a first actual measurement value of oxygen saturation of a tissue to be observed. A reference value calculation unit calculates a first reference value of the oxygen saturation of the tissue to be observed. A relative value calculation unit calculates a relative value of the first actual measurement value with reference to the first reference value. An image generation unit generates an image of the relative value of the first actual measurement value on the basis of an evaluation color table to generate an evaluation oxygen-saturation image. A display unit displays the evaluation oxygen-saturation image.