There are provided a medical image processing device, an endoscope system, a medical image processing method, and a program which detect an optimal lesion region according to an in-vivo position of a captured image. Images at a plurality of in-vivo positions of a subject are acquired from medical equipment that sequentially captures and displays in real time the images; positional information indicating the in-vivo position of the acquired image is acquired; from among a plurality of region-of-interest detection units that detect a region of interest from an input image and correspond to the plurality of in-vivo positions, respectively, a region-of-interest detection unit corresponding to the position indicated by the positional information is selected; and the selected region-of-interest detection unit detects a region of interest from the acquired image.

Disclosed herein are methods of vascular and structural imaging using imaging systems and methods described, the methods comprising producing an image of the vasculature or structure by imaging fluorescence using an imaging system, the system comprising: i) one or more detectors configured to form a fluorescence image of the sample and form a visible image of the sample; ii) a light source configured to emit an excitation light to induce fluorescence from the sample; and iii) a plurality of optics arranged to: direct the excitation light toward the sample; and direct a fluorescent light and a visible light from the sample to the detector; wherein the excitation light and the fluorescence light are directed substantially coaxially.

An ingestible capsule for detecting cancerous and non-cancerous tissues in a colon of patient is disclosed. The capsule has a radiation source integrated into the capsule body for illuminating tissues within a colon of the patient. Tissues of the colon are irradiated with radiation from the radiation source to elicit a fluorescence response, and a photon detector measures photons of the fluorescence response. Intensity and fluorescence lifetime of the fluorescence response is determined based on measured photons. A system employing the capsule is configured to distinguish cancerous and non-cancerous tissues based on the determined fluorescence lifetime of the fluorescence response.

The disclosure extends to methods, devices, and systems for removing speckle from a coherent light source, such as laser light. The methods, devices, and systems help eliminate or reduce speckle introduced from a coherent light source, such as laser light, by utilizing the teachings and principles of the disclosure.

Offset illumination using multiple emitters in a fluorescence imaging system is described. A system includes an emitter for emitting pulses of electromagnetic radiation and an image sensor comprising a pixel array for sensing reflected electromagnetic radiation. The emitter comprises a first emitter and a second emitter for emitting different wavelengths of electromagnetic radiation. The system is such that at least a portion of the pulses of electromagnetic radiation emitted by the emitter comprises one or more of a hyperspectral emission, a fluorescence emission, and/or a laser mapping pattern.

A plurality of devices that provide examination/diagnosis and/or treatment benefits to a patient are presented. The device including a plurality of light sources that provide for the emission of light in a plurality of wavelength ranges, wherein the plurality of light sources are activated by a sensor, configured to determine a proximity of the device to a patient, to control an application of a voltage to selected one of the plurality of light sources for a predetermined time period.

A method for distinguishing plaque and calculus is provided. The method is used in a device and includes the following steps: emitting, by a blue light-emitting diode, blue light to illuminate teeth in an oral cavity, wherein the blue light is used to generate autofluorescence of plaque and calculus on the teeth; sensing, by an image sensor, the autofluorescence of plaque and calculus; and distinguishing, by a processor, a plaque area and a calculus area on the teeth based on the autofluorescence.

An endoscope is provided that includes a first component, a second component, a light source, an image capturing element, and a light guide. The second component has a proximal end and a distal end with proximal end coupled to the first component. The light source is integrated in the first component and includes a laser and a converter. The laser emits primary light and the converter converts the primary light at least partially into secondary light that has a different wavelength. The image capturing element is arranged at the distal end. The light guide has an optical fiber that extends through the second component. The converter is coupled to the proximal end such that the primary and secondary light is injected into the optical fiber, is conducted from the proximal end to the distal end, and emitted at the distal end.

An illumination source includes a laser driver unit configured to emit a plasma sustaining beam. An ingress collimator receives the plasma sustaining beam and produces a collimated ingress beam. A focusing optic receives the collimated ingress beam and produce a focused sustaining beam. A sealed lamp chamber contains an ionizable media that, once ignited, forms a high intensity light emitting plasma having a waist size smaller than 150 microns. The sealed lamp chamber further includes an ingress window configured to receive the focused sustaining beam and an egress window configured to emit the high intensity light. An ignition source is configured to ignite the ionizable media, and an exit fiber is configured to receive and convey the high intensity light. The high intensity light is white light with a black body spectrum, and the exit fiber has a diameter in the range of 200-500 micrometers.

Provided are a diagnostic imaging device, diagnostic imaging method, diagnostic imaging program, and learned model with which gastric cancer diagnosis can be carried out in real time during endoscopic examination performed using NBI in combination with a magnifying endoscope. The diagnostic imaging device comprises an endoscopic video image acquisition unit which emits narrow-band light at a subject's stomach and acquires an endoscopic video image captured while the stomach is in a state of magnified observation, and an estimation unit which uses a convolutional neural network, which has been caused to learn using gastric cancer images and non-gastric cancer images as training data, to estimate the presence of gastric cancer in the acquired endoscopic video image, and outputs estimation results.